In [58]:
import itertools
import logging
import os
import pickle
import gensim
from gensim.utils import smart_open, simple_preprocess
import numpy as np
from cltk.stop.greek.stops import STOPS_LIST
STOPS_LIST = [simple_preprocess(stop, deacc=True)[0] for stop in STOPS_LIST if len(simple_preprocess(stop, deacc=True)) > 0]
logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)
logging.root.level = logging.INFO # ipython sometimes messes up the logging setup; restore
In [9]:
# Load pre-saved BoW
# Save BoW
user_dir = os.path.expanduser('~/cltk_data/user_data/')
try:
os.makedirs(user_dir)
except FileExistsError:
pass
bow_path = os.path.join(user_dir, 'bow_lda_gensim.mm')
mm_corpus = gensim.corpora.MmCorpus(bow_path)
print(mm_corpus)
MmCorpus(1484 documents, 84120 features, 3961081 non-zero entries)
In [10]:
print(next(iter(mm_corpus)))
[(0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0), (4, 1.0), (5, 1.0), (6, 1.0), (7, 2.0), (8, 1.0), (9, 1.0), (10, 1.0), (11, 1.0), (12, 1.0), (13, 1.0), (14, 1.0), (15, 1.0), (16, 1.0), (17, 1.0), (18, 1.0), (19, 1.0), (20, 1.0), (21, 1.0), (22, 1.0), (23, 1.0), (24, 1.0), (25, 1.0), (26, 1.0), (27, 1.0), (28, 1.0), (29, 1.0), (30, 1.0), (31, 1.0), (32, 1.0), (33, 1.0), (34, 1.0), (35, 1.0), (36, 1.0), (37, 1.0), (38, 1.0), (39, 1.0), (40, 1.0), (41, 1.0), (42, 1.0), (43, 1.0), (44, 1.0), (45, 1.0), (46, 1.0), (47, 1.0), (48, 2.0), (49, 1.0), (50, 1.0), (51, 1.0), (52, 1.0), (53, 1.0), (54, 1.0), (55, 1.0), (56, 1.0), (57, 1.0), (58, 1.0), (59, 1.0), (60, 1.0), (61, 1.0), (62, 1.0), (63, 1.0), (64, 1.0), (65, 1.0), (66, 1.0), (67, 1.0), (68, 1.0), (69, 1.0)]
In [15]:
# Save for reuse
with open(os.path.expanduser('~/cltk_data/user_data/tlg_bow_id2word.dict'), 'rb') as file_open:
id2word_tlg = pickle.load(file_open)
In [16]:
# Quick testing using just a part of the corpus
# use fewer documents during training, LDA is slow
clipped_corpus = gensim.utils.ClippedCorpus(mm_corpus, 100)
%time lda_model = gensim.models.LdaModel(clipped_corpus,
num_topics=10,
id2word=id2word_tlg,
passes=4)
CPU times: user 4min 16s, sys: 54.4 s, total: 5min 10s
Wall time: 4min 7s
In [18]:
lda_model.print_topics(-1) # print a few most important words for each LDA topic
Out[18]:
[(0,
'0.001*"διαφωνει" + 0.001*"διακεκαυμενη" + 0.000*"εναλλαγαι" + 0.000*"θερινη" + 0.000*"βουλομεθα" + 0.000*"οικετων" + 0.000*"καιροι" + 0.000*"λυσαωνθειηδεση" + 0.000*"ωργισμενους" + 0.000*"δεομενω"'),
(1,
'0.002*"νενευκασιν" + 0.001*"αδωμος" + 0.001*"μυουμενους" + 0.001*"νοτιωτερα" + 0.001*"διακειμενων" + 0.001*"αιστωτηριον" + 0.001*"προειρημενον" + 0.001*"επιταξ" + 0.001*"δηλουντος" + 0.001*"ασπετον"'),
(2,
'0.001*"αγνοιαν" + 0.001*"ακουσας" + 0.001*"ακρατον" + 0.001*"κινδυνευοντι" + 0.001*"λαμπτηρα" + 0.001*"εγενετο" + 0.000*"στοιχους" + 0.000*"ερωτος" + 0.000*"λαβομενη" + 0.000*"εναγισαντες"'),
(3,
'0.000*"ωργισμενους" + 0.000*"διαβοσκεσθαι" + 0.000*"απολυοντος" + 0.000*"ετοιμως" + 0.000*"σαρδονυχες" + 0.000*"νεα" + 0.000*"συμβαινει" + 0.000*"διαφωνει" + 0.000*"εξηρτημενων" + 0.000*"πενεσται"'),
(4,
'0.000*"νημερτεστατε" + 0.000*"φιλολογος" + 0.000*"απλανεσιν" + 0.000*"δηλαδη" + 0.000*"επιπλοκης" + 0.000*"θρασυλλωι" + 0.000*"διαβοσκεσθαι" + 0.000*"επωνομαζετο" + 0.000*"κρειτ" + 0.000*"φερωνυμον"'),
(5,
'0.001*"ρυπος" + 0.001*"περιτρεπομενα" + 0.000*"καταπελταισι" + 0.000*"νοτιωτερα" + 0.000*"σικελιωτας" + 0.000*"διακειμενων" + 0.000*"αποκτενουσι" + 0.000*"αποπλυσεως" + 0.000*"καθιεσαν" + 0.000*"εχομεν"'),
(6,
'0.003*"διαβοσκεσθαι" + 0.001*"δηλαδη" + 0.001*"παρακαθηται" + 0.001*"τυχης" + 0.000*"σχηματιζομενου" + 0.000*"εμφαινουσιν" + 0.000*"ανισχη" + 0.000*"πετευρον" + 0.000*"κεκληκας" + 0.000*"υποκριτη"'),
(7,
'0.000*"κριθαι" + 0.000*"συντεμνε" + 0.000*"καθαυανει" + 0.000*"κραυαλλιδαι" + 0.000*"καθισταντο" + 0.000*"εκκλητευεσθαι" + 0.000*"βαδιζουσιν" + 0.000*"πολυευκτον" + 0.000*"ταφον" + 0.000*"αρασκη"'),
(8,
'0.005*"σαμαιου" + 0.004*"λακεδαιμονα" + 0.003*"ωρμημενους" + 0.002*"εμπεδοκλεους" + 0.001*"αγομενης" + 0.001*"αναπεπεικως" + 0.001*"διηπορησθαι" + 0.001*"καθηστο" + 0.001*"βοηθησειαν" + 0.001*"βαινειν"'),
(9,
'0.001*"διαφωνει" + 0.001*"οικετων" + 0.001*"θερινη" + 0.001*"διακεκαυμενη" + 0.001*"αφοβοι" + 0.001*"απολαβουσα" + 0.001*"διεσφαλται" + 0.001*"εναλλαγαι" + 0.001*"καιροι" + 0.001*"δεομενω"')]
In [25]:
# first train tfidf model
# this modifies the feature weights of each word
%time tfidf_model = gensim.models.TfidfModel(mm_corpus, id2word=id2word_tlg)
INFO : collecting document frequencies
INFO : PROGRESS: processing document #0
INFO : calculating IDF weights for 1484 documents and 84119 features (3961081 matrix non-zeros)
CPU times: user 6.34 s, sys: 12 ms, total: 6.35 s
Wall time: 6.29 s
In [26]:
# then run lsi, which reduces dimensionality
%time lsi_model = gensim.models.LsiModel(tfidf_model[mm_corpus], id2word=id2word_tlg, num_topics=200)
INFO : using serial LSI version on this node
INFO : updating model with new documents
INFO : preparing a new chunk of documents
INFO : using 100 extra samples and 2 power iterations
INFO : 1st phase: constructing (1161925, 300) action matrix
INFO : orthonormalizing (1161925, 300) action matrix
INFO : 2nd phase: running dense svd on (300, 1484) matrix
INFO : computing the final decomposition
INFO : keeping 200 factors (discarding 19.480% of energy spectrum)
INFO : processed documents up to #1484
INFO : topic #0(5.232): 0.097*"καταπελταισι" + 0.096*"νοτιωτερα" + 0.093*"ηνεχυρασμενον" + 0.091*"διακειμενων" + 0.077*"αδωμος" + 0.075*"απωλειαν" + 0.071*"βεβαιωσον" + 0.066*"ροιζησαν" + 0.064*"οικειον" + 0.058*"λυσαωνθειηδεση"
INFO : topic #1(3.674): -0.138*"καταπελταισι" + -0.135*"νοτιωτερα" + -0.130*"διακειμενων" + -0.126*"ηνεχυρασμενον" + -0.106*"απωλειαν" + -0.092*"ροιζησαν" + 0.089*"μουνον" + -0.081*"βεβαιωσον" + -0.081*"οικειον" + -0.072*"νοτιωτεραν"
INFO : topic #2(3.159): 0.174*"νεφους" + -0.157*"αδωμος" + 0.108*"κινουντος" + 0.105*"σχηματισμους" + 0.098*"χορδης" + 0.093*"οφρυσι" + 0.093*"μαχεσαμενος" + -0.092*"ειρημενας" + 0.090*"εκαστ" + 0.084*"αναβαινειν"
INFO : topic #3(3.074): -0.162*"αδωμος" + 0.128*"μουνον" + -0.101*"ειρημενας" + 0.097*"ηνεχυρασμενον" + 0.096*"καταπελταισι" + -0.085*"επιταξ" + -0.076*"διεσφαλται" + 0.074*"νοτιωτερα" + -0.071*"ενθεντι" + 0.069*"διακειμενων"
INFO : topic #4(2.909): 0.343*"νεφους" + 0.219*"κινουντος" + 0.207*"σχηματισμους" + 0.162*"κατηγορουμενοις" + 0.153*"συμπεριφεροντος" + 0.139*"εμβαλλοντων" + 0.135*"κινηθεισης" + 0.123*"εμπεδοκλεους" + 0.112*"υποπτευσας" + 0.104*"διεκαρτερουν"
CPU times: user 4min 2s, sys: 1min 4s, total: 5min 7s
Wall time: 58.3 s
In [27]:
# for the first doc of the TLG corpus, here are the LSI scores for each of the 200 topics
print(next(iter(lsi_model[tfidf_model[mm_corpus]])))
[(0, 0.024289037065511311), (1, 0.014077893150839215), (2, -0.019146078135534279), (3, -0.018767966018989272), (4, -0.00099884333481120103), (5, -0.0051913135174345487), (6, -0.0014277098382469583), (7, -0.018818824919966906), (8, 0.0056374952350573453), (9, -0.0023221873083177956), (10, -0.0081668241786406002), (11, -0.019996384848007791), (12, 0.014338300486609062), (13, -0.00010303379864312642), (14, 0.0021088994788161335), (15, 0.0098757069667030732), (16, 0.0090732013832107734), (17, 0.0081570602885538118), (18, -0.0098302970910077361), (19, 0.01134303047809858), (20, -0.0042868477183203814), (21, -0.0019021128579354079), (22, 0.0083599609888375184), (23, -0.0024645892529614947), (24, -0.0089570567476525975), (25, 0.013241931401700954), (26, -0.0079806310637484165), (27, 0.0037053228065251193), (28, 0.002579960226988357), (29, 0.016675254728906144), (30, 0.001139717059664551), (31, 0.017415051948121849), (32, 0.02155588300179399), (33, -0.014399643194274744), (34, 0.0086417821638376194), (35, 0.0019196239528506589), (36, -0.0092597490356428752), (37, 0.0089426993994727588), (38, -0.0033247352021466121), (39, 0.0043252563456979976), (40, -0.011600487050304897), (41, -0.01846719773163864), (42, 0.022455690694824455), (43, 0.022284653628220616), (44, 0.013154858355956148), (45, 0.0060782591915144214), (46, -0.0016289526320537135), (47, -0.011025355268317685), (48, -0.0027855959607458523), (49, 0.02341407412140431), (50, 0.014168228048361773), (51, -0.020913766294840489), (52, 0.011047621207474528), (53, -0.018608771532384712), (54, 0.0032687890463270809), (55, -0.018303059103671113), (56, -0.012695703310685598), (57, -0.011013427637234821), (58, 0.012171665918386469), (59, -0.020884455633780889), (60, -0.016221940544241264), (61, 0.026301751352947104), (62, 0.0054090579316186903), (63, -0.0027071402193877144), (64, -0.022157738880452665), (65, -0.014876879211616954), (66, -0.026786633917908264), (67, 0.03372108459209), (68, 0.020898944380512773), (69, -0.03150335738846087), (70, -0.046912156645010701), (71, 0.058477051178594822), (72, -0.0085459131777686579), (73, 0.044878905745141133), (74, -0.0066645251236018589), (75, 0.019032190059046317), (76, 0.024559821236263511), (77, 0.039690432414167379), (78, 0.074787766817247456), (79, -0.030135061860844321), (80, 0.043865505194683359), (81, 0.0081650970611938847), (82, 0.015017961907571317), (83, 0.089624587973180597), (84, -0.0024169583280918621), (85, -0.063854837180863017), (86, 0.01702852613437502), (87, 0.060034330119294543), (88, -0.011884735652686492), (89, 0.052095335583462167), (90, 0.021777910674368968), (91, -0.060785209883194996), (92, 0.0056690151834923328), (93, 0.021440607789719679), (94, -0.032681247027792973), (95, -0.015307085816714019), (96, -0.067127004018173911), (97, 0.0063527004900622887), (98, -0.018713896483319333), (99, 0.04729387955172034), (100, 0.049798358172712928), (101, 6.7716286692901786e-05), (102, -0.026791823302928449), (103, 0.0052587258970216042), (104, -0.017042451797333616), (105, -0.0052389160290834914), (106, -0.026163936886468082), (107, -0.0767990213892073), (108, -0.0023793156419623968), (109, 0.0098681154742909019), (110, 0.010885509648405919), (111, -0.035294572931547162), (112, -0.039976551356029733), (113, 0.010018581180328926), (114, 0.023537700675056603), (115, 0.0050904661097621482), (116, -0.058000585273994036), (117, 0.056860577941296585), (118, -0.078851343136685542), (119, 0.0066815279366649188), (120, -0.025985889730822123), (121, 0.0028833542765728656), (122, 0.016628692020264686), (123, -0.010973547079998697), (124, 0.03596237206484295), (125, 0.0066949673903638545), (126, -0.046108452050849222), (127, -0.009584559662709909), (128, -0.003906535407959647), (129, 0.055890980023842879), (130, 0.036527627248751762), (131, -0.019154548880901807), (132, -0.0034278144498435439), (133, 0.012270017959773673), (134, 0.0066279395048268273), (135, 0.0054600064679576699), (136, -0.065398188345577646), (137, 0.010568851643894419), (138, 0.043787906725997972), (139, 0.031118225925865599), (140, -0.0038173277398371105), (141, 0.032562274346574674), (142, 0.025075353777096149), (143, 0.0030116215314162048), (144, 0.0088033154508956376), (145, -0.058045099958953059), (146, -0.095406527134466873), (147, -0.0067072082067343164), (148, -0.0093295508188511646), (149, -0.029709727100738353), (150, 0.036291585861045021), (151, -0.056689606559027272), (152, 0.027452149226681405), (153, -0.047811689346804215), (154, -0.026197492260465857), (155, 0.0075287508346123218), (156, 0.037001604153278862), (157, -0.010400155528468649), (158, 0.023953095420664296), (159, 0.0037034920083958171), (160, 0.00053725279403194436), (161, 0.015659773321151541), (162, -0.030265228915870839), (163, 0.039262938323316697), (164, 0.014426796514419173), (165, 0.011725282966390551), (166, 0.0062815109540450692), (167, -0.0071319311080869719), (168, -0.0069989416276920145), (169, -0.021197556499485414), (170, 0.014764201487785362), (171, -0.010038249382199516), (172, -0.042300066694992311), (173, -0.049921745256108666), (174, -0.044292960171272357), (175, 0.014122371017874424), (176, 0.006906579478159848), (177, 0.071401881488522792), (178, 0.027964961826863859), (179, -0.022687845574579771), (180, 0.054643703774599997), (181, 0.018264228030616739), (182, -0.076369221161988868), (183, -0.056267332983977555), (184, 0.06273243714353692), (185, 0.017192203723139864), (186, -0.037432485929905175), (187, 0.02216814711201694), (188, 0.026635450413089352), (189, 0.06404872471935405), (190, -0.021417420320947393), (191, 0.037750874763913034), (192, 0.016211567630521319), (193, 0.0070873968492201631), (194, -0.0032602427750246603), (195, -0.0099170031067420984), (196, 0.020476544105782313), (197, -0.017502506085859453), (198, -0.026180755219316114), (199, 0.01823831445711278)]
In [30]:
# cache the transformed corpora to disk, for use in later notebooks
path_lda = os.path.join(user_dir, 'gensim_tlg_lda.mm')
path_tfidf = os.path.join(user_dir, 'gensim_tlg_tfidf.mm')
path_lsi= os.path.join(user_dir, 'gensim_tlg_lsa.mm')
%time gensim.corpora.MmCorpus.serialize(path_lda, lda_model[mm_corpus])
%time gensim.corpora.MmCorpus.serialize(path_tfidf, tfidf_model[mm_corpus])
%time gensim.corpora.MmCorpus.serialize(path_lsi, lsi_model[tfidf_model[mm_corpus]])
INFO : storing corpus in Matrix Market format to /home/kyle/cltk_data/user_data/gensim_tlg_lda.mm
INFO : saving sparse matrix to /home/kyle/cltk_data/user_data/gensim_tlg_lda.mm
INFO : PROGRESS: saving document #0
INFO : PROGRESS: saving document #1000
INFO : saved 1484x10 matrix, density=56.617% (8402/14840)
INFO : saving MmCorpus index to /home/kyle/cltk_data/user_data/gensim_tlg_lda.mm.index
INFO : storing corpus in Matrix Market format to /home/kyle/cltk_data/user_data/gensim_tlg_tfidf.mm
INFO : saving sparse matrix to /home/kyle/cltk_data/user_data/gensim_tlg_tfidf.mm
INFO : PROGRESS: saving document #0
CPU times: user 24.2 s, sys: 1min 11s, total: 1min 35s
Wall time: 12.6 s
INFO : PROGRESS: saving document #1000
INFO : saved 1484x84120 matrix, density=3.173% (3961081/124834080)
INFO : saving MmCorpus index to /home/kyle/cltk_data/user_data/gensim_tlg_tfidf.mm.index
INFO : storing corpus in Matrix Market format to /home/kyle/cltk_data/user_data/gensim_tlg_lsa.mm
INFO : saving sparse matrix to /home/kyle/cltk_data/user_data/gensim_tlg_lsa.mm
CPU times: user 14 s, sys: 364 ms, total: 14.4 s
Wall time: 14 s
INFO : PROGRESS: saving document #0
INFO : PROGRESS: saving document #1000
INFO : saved 1484x200 matrix, density=100.000% (296800/296800)
INFO : saving MmCorpus index to /home/kyle/cltk_data/user_data/gensim_tlg_lsa.mm.index
CPU times: user 9.81 s, sys: 112 ms, total: 9.92 s
Wall time: 9.9 s
In [42]:
# LDA
def tokenize(text):
# https://radimrehurek.com/gensim/utils.html#gensim.utils.simple_preprocess
tokens = [token for token in simple_preprocess(text, deacc=True)]
return [token for token in tokens if token not in STOPS_LIST]
doc = "ἐπειδὴ πᾶσαν πόλιν ὁρῶμεν κοινωνίαν τινὰ οὖσαν καὶ πᾶσαν κοινωνίαν ἀγαθοῦ τινος ἕνεκεν συνεστηκυῖαν （τοῦ γὰρ εἶναι δοκοῦντος ἀγαθοῦ χάριν πάντα πράττουσι πάντες）, δῆλον ὡς πᾶσαι μὲν ἀγαθοῦ τινος στοχάζονται, μάλιστα δὲ [5] καὶ τοῦ κυριωτάτου πάντων ἡ πασῶν κυριωτάτη καὶ πάσας περιέχουσα τὰς ἄλλας. αὕτη δ᾽ ἐστὶν ἡ καλουμένη πόλις καὶ ἡ κοινωνία ἡ πολιτική. ὅσοι μὲν οὖν οἴονται πολιτικὸν καὶ βασιλικὸν καὶ οἰκονομικὸν καὶ δεσποτικὸν εἶναι τὸν αὐτὸν οὐ καλῶς λέγουσιν （πλήθει γὰρ καὶ ὀλιγότητι νομίζουσι [10] διαφέρειν ἀλλ᾽ οὐκ εἴδει τούτων ἕκαστον, οἷον ἂν μὲν ὀλίγων, δεσπότην, ἂν δὲ πλειόνων, οἰκονόμον, ἂν δ᾽ ἔτι πλειόνων, πολιτικὸν ἢ βασιλικόν, ὡς οὐδὲν διαφέρουσαν μεγάλην οἰκίαν ἢ μικρὰν πόλιν: καὶ πολιτικὸν δὲ καὶ βασιλικόν, ὅταν μὲν αὐτὸς ἐφεστήκῃ, βασιλικόν, ὅταν [15] δὲ κατὰ τοὺς λόγους τῆς ἐπιστήμης τῆς τοιαύτης κατὰ μέρος ἄρχων καὶ ἀρχόμενος, πολιτικόν: ταῦτα δ᾽ οὐκ ἔστιν ἀληθῆ）: δῆλον δ᾽ ἔσται τὸ λεγόμενον ἐπισκοποῦσι κατὰ τὴν ὑφηγημένην μέθοδον. ὥσπερ γὰρ ἐν τοῖς ἄλλοις τὸ σύνθετον μέχρι τῶν ἀσυνθέτων ἀνάγκη διαιρεῖν （ταῦτα γὰρ ἐλάχιστα [20] μόρια τοῦ παντός）, οὕτω καὶ πόλιν ἐξ ὧν σύγκειται σκοποῦντες ὀψόμεθα καὶ περὶ τούτων μᾶλλον, τί τε διαφέρουσιν ἀλλήλων καὶ εἴ τι τεχνικὸν ἐνδέχεται λαβεῖν περὶ ἕκαστον τῶν ῥηθέντων."
doc = ' '.join(simple_preprocess(doc))
In [43]:
# transform text into the bag-of-words space
bow_vector = id2word_tlg.doc2bow(tokenize(doc))
print([(id2word_tlg[id], count) for id, count in bow_vector])
[('αυτη', 1), ('τινα', 1), ('τοις', 1), ('ουσαν', 1), ('τινος', 2), ('ουδεν', 1), ('τας', 1), ('πολιν', 3), ('παντες', 1), ('παντα', 1), ('αλλας', 1), ('ειναι', 2), ('πασαν', 2), ('παντων', 1), ('λεγουσιν', 1), ('αυτον', 1), ('ων', 1), ('μερος', 1), ('ουτω', 1), ('οιον', 1), ('εστιν', 2), ('δηλον', 2), ('λαβειν', 1), ('ταυτα', 2), ('διαφερουσαν', 1), ('λογους', 1), ('ωσπερ', 1), ('πολις', 1), ('τουτων', 2), ('ρηθεντων', 1), ('οταν', 2), ('μαλλον', 1), ('λεγομενον', 1), ('επειδη', 1), ('αληθη', 1), ('πραττουσι', 1), ('χαριν', 1), ('οικιαν', 1), ('μαλιστα', 1), ('αλλοις', 1), ('ενεκεν', 1), ('καλουμενη', 1), ('μεγαλην', 1), ('εκαστον', 2), ('αρχων', 1), ('αλληλων', 1), ('μεχρι', 1), ('πληθει', 1), ('οιονται', 1), ('παντος', 1), ('συνθετον', 1), ('αρχομενος', 1), ('κοινωνιαν', 2), ('τοιαυτης', 1), ('καλως', 1), ('πλειονων', 2), ('νομιζουσι', 1), ('αναγκη', 1), ('πασας', 1), ('περιεχουσα', 1), ('δεσποτην', 1), ('πασαι', 1), ('οσοι', 1), ('μεθοδον', 1), ('ειδει', 1), ('επιστημης', 1), ('πασων', 1), ('ελαχιστα', 1), ('εσται', 1), ('διαφερειν', 1), ('συγκειται', 1), ('διαφερουσιν', 1), ('ορωμεν', 1), ('αγαθου', 3), ('σκοπουντες', 1), ('ενδεχεται', 1), ('βασιλικον', 4), ('ολιγων', 1), ('οικονομικον', 1), ('πολιτικη', 1), ('τεχνικον', 1), ('μορια', 1), ('κοινωνια', 1), ('πολιτικον', 4), ('δοκουντος', 1), ('στοχαζονται', 1), ('μικραν', 1), ('οικονομον', 1), ('κυριωτατου', 1), ('ολιγοτητι', 1), ('επισκοπουσι', 1), ('οψομεθα', 1), ('διαιρειν', 1), ('δεσποτικον', 1), ('συνεστηκυιαν', 1), ('κυριωτατη', 1), ('ασυνθετων', 1), ('εφεστηκη', 1), ('υφηγημενην', 1)]
In [45]:
# transform into LDA space
lda_vector = lda_model[bow_vector]
print(lda_vector)
[(2, 0.62834060088914145), (4, 0.075956794886348669), (5, 0.07265613621925443), (7, 0.036576776832934622), (9, 0.18233695136950517)]
0.001*"αγνοιαν" + 0.001*"ακουσας" + 0.001*"ακρατον" + 0.001*"κινδυνευοντι" + 0.001*"λαμπτηρα" + 0.001*"εγενετο" + 0.000*"στοιχους" + 0.000*"ερωτος" + 0.000*"λαβομενη" + 0.000*"εναγισαντες"
In [46]:
# print the document's single most prominent LDA topic
print(lda_model.print_topic(max(lda_vector, key=lambda item: item[1])[0]))
0.001*"αγνοιαν" + 0.001*"ακουσας" + 0.001*"ακρατον" + 0.001*"κινδυνευοντι" + 0.001*"λαμπτηρα" + 0.001*"εγενετο" + 0.000*"στοιχους" + 0.000*"ερωτος" + 0.000*"λαβομενη" + 0.000*"εναγισαντες"
In [47]:
# transform into LSI space
lsi_vector = lsi_model[tfidf_model[bow_vector]]
print(lsi_vector)
[(0, 0.024566044502988167), (1, 0.015083771441513003), (2, -0.0053303162617581688), (3, -0.0049469294214897746), (4, -0.004304577822397548), (5, -0.0019232322736995517), (6, 0.0012844223351456006), (7, -0.014361304541287518), (8, 0.0029628230116745817), (9, -0.001991805827179696), (10, -0.0038961163189403162), (11, -0.0017520317514794631), (12, -0.003289379651920948), (13, 0.013984478154812633), (14, -0.0042467976096142246), (15, -0.001134755589540278), (16, -3.7748879427212047e-05), (17, 0.0026908339487799688), (18, 0.0008648525754869072), (19, 0.0041513971222094598), (20, -0.0089440015914239723), (21, -0.0039957917322863508), (22, -0.0039182934453001663), (23, -0.0016527724004994603), (24, -0.00052822506831432989), (25, 0.0080844104724639085), (26, -0.0062247602598571213), (27, -0.0044917328067377452), (28, -0.0037489807849336193), (29, 0.00082110267265825731), (30, -0.00062982415302895579), (31, 0.0012224945919592724), (32, 0.00034178180065024514), (33, -0.0035051594171728109), (34, 0.0015871010184025072), (35, 0.00886285826968412), (36, 0.0015818548665881955), (37, 0.0037859016589060698), (38, -0.012620161191359631), (39, 0.0018526745552406249), (40, 0.00010616271065914951), (41, -0.00043480504669294722), (42, 0.0028126590225130347), (43, -0.0032230545374458161), (44, 0.0026409627801670739), (45, 0.0046014336305242876), (46, -0.0046718624808623713), (47, 0.0018707868792217019), (48, 0.0077525623538176047), (49, 0.0045556965081330666), (50, 0.0036950529558445661), (51, 0.0091185390984539379), (52, 0.0019388491547395331), (53, 0.0014590691575313819), (54, -5.1785925271862241e-05), (55, -0.012028759302179963), (56, 0.0025692826266982462), (57, 0.0021493354000374763), (58, 0.0083313615275431054), (59, 0.006687962755075101), (60, -0.0023625250664593444), (61, -0.0026501852358160423), (62, 0.0023355850108483221), (63, -0.0028537923251027606), (64, 0.0043700109389321062), (65, 0.0019705324663704502), (66, -0.0037402372057628946), (67, -0.0037860993320776642), (68, 0.0050107932971979862), (69, 0.0046186676196326593), (70, -0.0010204768752816846), (71, 0.0021388002012219676), (72, -0.0056905356024502026), (73, 0.0010473096280735875), (74, -0.00018164833137073035), (75, 0.0046147802068960027), (76, 0.011385559054764072), (77, 0.0046419784587952992), (78, 0.0056621163066387786), (79, -0.0018929254413337395), (80, 0.0077554211573622276), (81, -0.0080634908329092637), (82, 0.002075538618951633), (83, 0.012845499870491472), (84, -0.0041808689431154485), (85, -0.0071614496840741093), (86, 0.0043144938028403818), (87, 0.0064232304933360658), (88, 0.0024669964944728016), (89, 0.0039614522391635291), (90, 0.0092257710452432785), (91, -0.0031991083375173179), (92, 0.00078631273898332067), (93, 0.00078526391856236519), (94, -0.010032010579283861), (95, -0.00095840887798846709), (96, -0.012352320753852173), (97, -0.00058431082889679148), (98, -0.002352133722427859), (99, 0.0054619597142351924), (100, 0.0051982676194122363), (101, -0.0065979727934071287), (102, -0.013715282053422019), (103, 0.0016596702826770452), (104, -0.0008552484785012772), (105, 0.004197240331888067), (106, -0.0016978241588646613), (107, -0.025087985342604886), (108, -0.00064620077636295178), (109, -0.0011332408051378878), (110, -0.00028525949809647233), (111, -0.0058307306981414597), (112, -0.0024681990087327831), (113, 0.0078778832421350763), (114, 0.0041868969898320391), (115, 0.0024715224898860817), (116, 0.0013392045122846931), (117, 0.0029802132376560899), (118, -0.0071874572164885506), (119, -0.008940039008716788), (120, -0.0063636357726013825), (121, 0.00063212524592311833), (122, -0.0041780521363688579), (123, -0.0030065308214962875), (124, -0.0070638632336674769), (125, -0.0015293446312161863), (126, -0.00085125740720103343), (127, 0.0059825429562803453), (128, 0.00076248453476272554), (129, 0.012848109343053795), (130, 0.0060729577367351949), (131, -0.00010739129420822793), (132, 0.0058684064311142984), (133, -0.00010011173263057786), (134, 0.002634608867195127), (135, 0.0021748258464612262), (136, -0.0095187771558906138), (137, 0.0057887774306556892), (138, 0.0087441351905820936), (139, 0.0095878045788447205), (140, -0.0071526367622581338), (141, 0.0065770587182183097), (142, -0.00070598793759336616), (143, 0.0097890591649815208), (144, 0.0020843313646245272), (145, -0.0044474828351236655), (146, -0.0045919919568516111), (147, -0.00031376017189617736), (148, 0.0071559611385200944), (149, 0.000869595335045578), (150, 0.001341390063957509), (151, 0.0020632854947299105), (152, -0.00039826494075980698), (153, 0.0047741946949060553), (154, 0.0050021176959970373), (155, -0.0028257092508001359), (156, -0.00018687104480685115), (157, -0.0048241206291642072), (158, 0.00013092019882082647), (159, 0.0036675219195617514), (160, -0.0035434795549058348), (161, 0.0031775120544703206), (162, 0.006078514741842918), (163, -0.0007177896492420999), (164, 0.0065601045161077206), (165, -0.0018291303577856132), (166, -0.0090675822685208178), (167, -0.01284395230589533), (168, 0.0045699946281729685), (169, -0.0078246171460543239), (170, 0.0070315452516737584), (171, -0.0012919581822720767), (172, -0.0043678814142274567), (173, -0.0087342619308846264), (174, 0.0013336803358781344), (175, 0.000656293964381333), (176, 0.0021195427620709067), (177, 0.001892845505530046), (178, -0.00067712038179216832), (179, -0.0065088032386783042), (180, -0.0019126625286001152), (181, 0.0038603289789335619), (182, -0.0086495815069853009), (183, -0.0063446812719078691), (184, 0.0029427204844722308), (185, 0.0028366787503315877), (186, -0.0018581599880312127), (187, -0.0010213580592976046), (188, 0.0023436716855686772), (189, 0.0054467466548237097), (190, -0.003910104801220126), (191, -0.0055422139793410959), (192, -0.004042792455767631), (193, -0.0029468760945620667), (194, -0.0037368916195641482), (195, -0.0017073303289754436), (196, 0.0010949616688410671), (197, 0.0035706512771595338), (198, 0.0092135112909515638), (199, -0.0064916607244927598)]
In [48]:
# print the document's single most prominent LSI topic (not interpretable like LDA!)
print(lsi_model.print_topic(max(lsi_vector, key=lambda item: abs(item[1]))[0]))
0.140*"περιαυχενιον" + -0.082*"λεγοιεν" + 0.072*"απονοηθεντων" + -0.065*"κριθαι" + -0.064*"κλαπεντας" + 0.063*"ζωνας" + -0.062*"περιπαθης" + -0.061*"αμμωνος" + 0.061*"λυσικλει" + 0.061*"λαμβανοντα"
In [49]:
path_lda = os.path.join(user_dir, 'gensim_tlg_lda.model')
path_tfidf = os.path.join(user_dir, 'gensim_tlg_tfidf.model')
path_lsi= os.path.join(user_dir, 'gensim_tlg_lsa.model')
# store all trained models to disk
lda_model.save(path_lda)
lsi_model.save(path_lsi)
tfidf_model.save(path_tfidf)
INFO : saving LdaState object under /home/kyle/cltk_data/user_data/gensim_tlg_lda.model.state, separately None
INFO : storing np array 'sstats' to /home/kyle/cltk_data/user_data/gensim_tlg_lda.model.state.sstats.npy
INFO : saved /home/kyle/cltk_data/user_data/gensim_tlg_lda.model.state
INFO : saving LdaModel object under /home/kyle/cltk_data/user_data/gensim_tlg_lda.model, separately ['expElogbeta', 'sstats']
INFO : storing np array 'expElogbeta' to /home/kyle/cltk_data/user_data/gensim_tlg_lda.model.expElogbeta.npy
INFO : not storing attribute dispatcher
INFO : not storing attribute id2word
INFO : not storing attribute state
INFO : saved /home/kyle/cltk_data/user_data/gensim_tlg_lda.model
INFO : saving Projection object under /home/kyle/cltk_data/user_data/gensim_tlg_lsa.model.projection, separately None
INFO : storing np array 'u' to /home/kyle/cltk_data/user_data/gensim_tlg_lsa.model.projection.u.npy
INFO : saved /home/kyle/cltk_data/user_data/gensim_tlg_lsa.model.projection
INFO : saving LsiModel object under /home/kyle/cltk_data/user_data/gensim_tlg_lsa.model, separately None
INFO : not storing attribute projection
INFO : not storing attribute dispatcher
INFO : saved /home/kyle/cltk_data/user_data/gensim_tlg_lsa.model
INFO : saving TfidfModel object under /home/kyle/cltk_data/user_data/gensim_tlg_tfidf.model, separately None
INFO : saved /home/kyle/cltk_data/user_data/gensim_tlg_tfidf.model
Read insighful intro at http://radimrehurek.com/topic_modeling_tutorial/2%20-%20Topic%20Modeling.html#Evaluation
For each trained topic, they take its first ten words, then substitute one of them with another, randomly chosen word (intruder!) and see whether a human can reliably tell which one it was. If so, the trained topic is topically coherent (good); if not, the topic has no discernible theme (bad)
In [54]:
# select top 50 words for each of the 20 LDA topics
top_words = [[word for word, _ in lda_model.show_topic(topicno, topn=50)] for topicno in range(lda_model.num_topics)]
print(top_words)
[['διαφωνει', 'διακεκαυμενη', 'εναλλαγαι', 'θερινη', 'βουλομεθα', 'οικετων', 'καιροι', 'λυσαωνθειηδεση', 'ωργισμενους', 'δεομενω', 'απολαβουσα', 'αφοβοι', 'διεσφαλται', 'αντεστραμμενην', 'πεντακοσιων', 'ευτελων', 'αποτυφλωσιν', 'συμμαρτυρει', 'νεκρωσιν', 'αγχονη', 'δυνατωτερου', 'αγορητης', 'διεγγυηθεισα', 'εκουσιως', 'θα', 'μελικερτης', 'εγγραφηναι', 'απολλω', 'πολλοστον', 'συγκριτικον', 'οριζοντα', 'υπειπον', 'ελκτικη', 'ψαλτριων', 'αξιοπιστω', 'διαβολην', 'επανελαβεν', 'πολυφημην', 'εξισασουσι', 'εριζει', 'πυθαγορειων', 'μεμνησθε', 'κεγχρεων', 'δηλουντος', 'ολιγως', 'βασεσιν', 'αυριον', 'μηθενος', 'αρκτου', 'μανθανοντας'], ['νενευκασιν', 'αδωμος', 'μυουμενους', 'νοτιωτερα', 'διακειμενων', 'αιστωτηριον', 'προειρημενον', 'επιταξ', 'δηλουντος', 'ασπετον', 'ζωδιακος', 'αντεστραμμενην', 'νοτιωτεραν', 'απωλειαν', 'ειρημενας', 'ενθεντι', 'ανετελλεν', 'αλλοφυλω', 'συνουσιαν', 'μεταλλα', 'αμυριος', 'αφορωσης', 'κωπηλατας', 'τριακοντ', 'ευφοριαν', 'προσδοκωση', 'πηγη', 'ιστορει', 'εδιδαξαμεν', 'αφοβοι', 'ιμασσων', 'αγεσθαι', 'ιμεραιου', 'δειπνησας', 'κυμαιων', 'θυσειν', 'ευεργετηθεισαι', 'ανεβιβασθη', 'κυριωτατης', 'εδωκεν', 'διδομενην', 'ακλητον', 'φασκουσα', 'προκατασκευας', 'λεπτην', 'δεηθεισης', 'μεχειρ', 'δυσηχεος', 'λυσαωνθειηδεση', 'πλανωμενας'], ['αγνοιαν', 'ακουσας', 'ακρατον', 'κινδυνευοντι', 'λαμπτηρα', 'εγενετο', 'στοιχους', 'ερωτος', 'λαβομενη', 'εναγισαντες', 'νυκτα', 'επιουση', 'εοντι', 'φρονηματα', 'κηρυξιν', 'τας', 'ευποριαν', 'μοισι', 'σεληνη', 'σειεται', 'διαφωνει', 'τριτατην', 'καταναλισκουσι', 'ξειντε', 'θηλαζει', 'αζητητον', 'ιουσα', 'ωφελουντο', 'μαρτυρειν', 'οθνειος', 'απειρεσιον', 'ειδοτα', 'διεπραξαμην', 'παρθεν', 'επυκαζε', 'εδει', 'κλεινοτατου', 'θεου', 'τριτην', 'δεσμων', 'διαβοσκεσθαι', 'αλλαχοθεν', 'θεοις', 'ουρανιης', 'εσκεπτετο', 'χαλεπη', 'δωροξενια', 'δειναν', 'οικειως', 'νυμφοδωρος'], ['ωργισμενους', 'διαβοσκεσθαι', 'απολυοντος', 'ετοιμως', 'σαρδονυχες', 'νεα', 'συμβαινει', 'διαφωνει', 'εξηρτημενων', 'πενεσται', 'ευνουχος', 'κατηγορων', 'νενευκασιν', 'οποιον', 'θερινη', 'παρεχοντος', 'κινδυνευοντι', 'οικετων', 'γνησιον', 'διακεκαυμενη', 'βηθηλη', 'αφρωδη', 'νοτιωτερα', 'σπουδιαν', 'αθανατου', 'θυσιας', 'μακεδονες', 'τραπεζιτη', 'αρχιππον', 'εκκλησιαστας', 'ενεκαλει', 'τιμη', 'κολλησις', 'επενεχθειη', 'κεχωρισται', 'συμβαλουμαι', 'επιτηδευειν', 'καιροι', 'προτερον', 'ακρα', 'λυσαωνθειηδεση', 'διεσφαλται', 'τριακαδα', 'μυουμενους', 'αντεστραμμενην', 'ζωδιακος', 'ευποριαν', 'κυνος', 'ακμαζοντες', 'λυπειται'], ['νημερτεστατε', 'φιλολογος', 'απλανεσιν', 'δηλαδη', 'επιπλοκης', 'θρασυλλωι', 'διαβοσκεσθαι', 'επωνομαζετο', 'κρειτ', 'φερωνυμον', 'επιτεταμενην', 'πρακτικου', 'ενενηκοστη', 'ξεναις', 'ηυτρεπισμενον', 'αμελες', 'αλως', 'ουθειϲ', 'επραττεν', 'ιστιοις', 'καρπιζεται', 'ζημιουμεν', 'χρηματων', 'λδʹ', 'πεφωραται', 'αναστησας', 'διπτυχ', 'αγαν', 'εκπνευσαι', 'οιομαι', 'εκγονοις', 'φηϲι', 'γραφας', 'ποιμναισι', 'γηθεν', 'ρειθροις', 'αρπασθεισα', 'ειλημενον', 'κατηγορησαντος', 'δαρδανος', 'αρετηι', 'εσθαιπ', 'κρηνων', 'ταπεινωματος', 'εμου', 'σαγηνης', 'ικτης', 'ομοιους', 'ασελγης', 'δλυαιος'], ['ρυπος', 'περιτρεπομενα', 'καταπελταισι', 'νοτιωτερα', 'σικελιωτας', 'διακειμενων', 'αποκτενουσι', 'αποπλυσεως', 'καθιεσαν', 'εχομεν', 'πωυ', 'ηνεχυρασμενον', 'ιπποθωντιδος', 'πολυτελεστατον', 'θανοντας', 'ηλικιας', 'υμετερ', 'οικειον', 'εκαστοτε', 'ημη', 'αιρομενων', 'σεμεωνιδα', 'οιδ', 'ευεργεσιης', 'ηγησαιτ', 'ηρτημενον', 'βοσπορου', 'τριτομηνις', 'χρωμενοις', 'πυλωροι', 'απερχομενων', 'φρονεεσκες', 'προστησαμενους', 'θησηιον', 'μυουμενους', 'κινησιας', 'θαυμασει', 'ικανωτατος', 'συνετριβε', 'επιμαστον', 'λανθανοντας', 'υπερηφανησοντας', 'ευκλεα', 'αχιτωβον', 'λυσιμελης', 'ερωτικος', 'ανεξιχνιαστοις', 'υψηλοτεραι', 'διομηδης', 'ομοιοις'], ['διαβοσκεσθαι', 'δηλαδη', 'παρακαθηται', 'τυχης', 'σχηματιζομενου', 'εμφαινουσιν', 'ανισχη', 'πετευρον', 'κεκληκας', 'υποκριτη', 'συριγγων', 'εκπνευσαι', 'τηλεφου', 'φλοιωτιν', 'λαμπτηροκλεπτης', 'καταγελαστα', 'στεγης', 'επειθ', 'γοων', 'δρυκαρπα', 'προυτιθει', 'προστυχοι', 'ανεχειν', 'αλιμους', 'λαμβανων', 'θρυλιγματων', 'λερνης', 'ταλαιπωρια', 'ωϲθ', 'απετυχε', 'αδωμος', 'τελειωθεντα', 'παθεσιν', 'περιφερειας', 'παρεικαζονται', 'παραγινομενους', 'ηεροειδες', 'ταξιαρχος', 'υφεσεως', 'περιοδου', 'διναισιν', 'ετερος', 'αρσις', 'εθεωρει', 'κυθνος', 'μετρουμενον', 'ευωνυμων', 'υμην', 'επιδεικτεον', 'δωροδοκιας'], ['κριθαι', 'συντεμνε', 'καθαυανει', 'κραυαλλιδαι', 'καθισταντο', 'εκκλητευεσθαι', 'βαδιζουσιν', 'πολυευκτον', 'ταφον', 'αρασκη', 'πλαναται', 'πεπορθηνται', 'νοτιωτερα', 'προκομισαντας', 'εγεφυρωσεν', 'επικειμενας', 'επιπονως', 'ποδων', 'διοικησαμενος', 'ανεδειξε', 'εκφεροντος', 'οικετας', 'νωχος', 'καλως', 'βαρουχον', 'ενιαυτον', 'προκαταγγειλαι', 'προβουλευσαν', 'τριακονταεννεα', 'αρωγους', 'σκηπτεσθαι', 'ομοπατριω', 'ελεηλατησε', 'λιγυρη', 'αμαθα', 'ιππεας', 'μολοττων', 'προειρηται', 'κατοικουντες', 'επεμενον', 'αναγκαιοτατων', 'κιρρας', 'λαβοντες', 'εωρακεναι', 'φιλιεις', 'κηρυκος', 'συρια', 'γραμματει', 'διακεκαυμενη', 'προκομιζων'], ['σαμαιου', 'λακεδαιμονα', 'ωρμημενους', 'εμπεδοκλεους', 'αγομενης', 'αναπεπεικως', 'διηπορησθαι', 'καθηστο', 'βοηθησειαν', 'βαινειν', 'σεξτον', 'απνους', 'διαμαρτυριας', 'λευκοις', 'νεφους', 'ελληνικη', 'κεχρισμενοι', 'παραλεξομαι', 'επωδας', 'ωκοδομησθαι', 'ηρακλει', 'μοισι', 'γαιος', 'πρασσεσθαι', 'υπαντησαντες', 'παραχειμασιαν', 'διημαρτεν', 'οποτ', 'αρης', 'ποινη', 'πολιορκουμενης', 'χαλκαις', 'αναγεγραμμενοις', 'καταπαυσαντες', 'εντεταμενως', 'δασυνομενον', 'βαρακου', 'υπακουσομενος', 'κασανδρος', 'αιγλης', 'χορδης', 'μετην', 'κρατερον', 'επικοι', 'πεπομφα', 'σανοτ', 'θερειης', 'κακομηχανον', 'δικτατωρ', 'πολυστονου'], ['διαφωνει', 'οικετων', 'θερινη', 'διακεκαυμενη', 'αφοβοι', 'απολαβουσα', 'διεσφαλται', 'εναλλαγαι', 'καιροι', 'δεομενω', 'βουλομεθα', 'ωργισμενους', 'λυσαωνθειηδεση', 'αγορητης', 'αποτυφλωσιν', 'οριζοντα', 'αξιοπιστω', 'επανελαβεν', 'διακοπης', 'εγγραφηναι', 'εκουσιως', 'εριζει', 'θα', 'πεντακοσιων', 'κεγχρεων', 'αγχονη', 'πολλοστον', 'ευτελων', 'δυνατωτερου', 'απολλω', 'προστακτικως', 'πυθαγορειων', 'αντεστραμμενην', 'νομους', 'συγκριτικον', 'ψαλτριων', 'διεγγυηθεισα', 'ελκτικη', 'νεκρωσιν', 'αυριον', 'συμμαρτυρει', 'μελικερτης', 'ευφημισμοι', 'βασεσιν', 'ολιγως', 'μανθανοντας', 'πολυφημην', 'εξισασουσι', 'θαυμασει', 'διαβολην']]
In [59]:
# get all top 50 words in all 20 topics, as one large set
all_words = set(itertools.chain.from_iterable(top_words))
print("Can you spot the misplaced word in each topic?")
# for each topic, replace a word at a different index, to make it more interesting
replace_index = np.random.randint(0, 10, lda_model.num_topics)
replacements = []
for topicno, words in enumerate(top_words):
other_words = all_words.difference(words)
replacement = np.random.choice(list(other_words))
replacements.append((words[replace_index[topicno]], replacement))
words[replace_index[topicno]] = replacement
print("%i: %s" % (topicno, ' '.join(words[:10])))
Can you spot the misplaced word in each topic?
0: προκομισαντας διακεκαυμενη εναλλαγαι θερινη βουλομεθα οικετων καιροι λυσαωνθειηδεση ωργισμενους δεομενω
1: νενευκασιν αδωμος μυουμενους νοτιωτερα διακειμενων διακεκαυμενη προειρημενον επιταξ δηλουντος ασπετον
2: αγνοιαν ακουσας ακρατον νωχος λαμπτηρα εγενετο στοιχους ερωτος λαβομενη εναγισαντες
3: ωργισμενους διαβοσκεσθαι απολυοντος ετοιμως σαρδονυχες παθεσιν συμβαινει διαφωνει εξηρτημενων πενεσται
4: νημερτεστατε φιλολογος απλανεσιν δηλαδη επιπλοκης θρασυλλωι διαβοσκεσθαι μανθανοντας κρειτ φερωνυμον
5: ρυπος περιτρεπομενα καταπελταισι διαφωνει σικελιωτας διακειμενων αποκτενουσι αποπλυσεως καθιεσαν εχομεν
6: διαβοσκεσθαι δηλαδη παρακαθηται τυχης σχηματιζομενου εμφαινουσιν ανισχη ελληνικη κεκληκας υποκριτη
7: κριθαι συντεμνε καθαυανει αναστησας καθισταντο εκκλητευεσθαι βαδιζουσιν πολυευκτον ταφον αρασκη
8: σαμαιου λακεδαιμονα ωρμημενους εμπεδοκλεους ακρατον αναπεπεικως διηπορησθαι καθηστο βοηθησειαν βαινειν
9: διαφωνει οικετων θερινη διακεκαυμενη αφοβοι απολαβουσα διεσφαλται εναλλαγαι πυλωροι δεομενω
In [60]:
print("Actual replacements were:")
print(list(enumerate(replacements)))
Actual replacements were:
[(0, ('διαφωνει', 'προκομισαντας')), (1, ('αιστωτηριον', 'διακεκαυμενη')), (2, ('κινδυνευοντι', 'νωχος')), (3, ('νεα', 'παθεσιν')), (4, ('επωνομαζετο', 'μανθανοντας')), (5, ('νοτιωτερα', 'διαφωνει')), (6, ('πετευρον', 'ελληνικη')), (7, ('κραυαλλιδαι', 'αναστησας')), (8, ('αγομενης', 'ακρατον')), (9, ('καιροι', 'πυλωροι'))]
In [68]:
# this function first defined in pt. 1
def iter_tlg(tlg_dir):
file_names = os.listdir(tlg_dir)
for file_name in file_names:
file_path = os.path.join(tlg_dir, file_name)
with open(file_path) as file_open:
file_read = file_open.read()
tokens = tokenize(file_read)
# ignore short docs
if len(tokens) < 50:
continue
yield file_name, tokens
# evaluate on 1k documents **not** used in LDA training
tlg_preprocessed = os.path.expanduser('~/cltk_data/greek/text/tlg/plaintext/')
doc_stream = (tokens for _, tokens in iter_tlg(tlg_preprocessed)) # generator
test_docs = list(itertools.islice(doc_stream, 100, 200)) # ['πανυ', 'καλως', ...], [...], ...]
In [93]:
def intra_inter(model, test_docs, num_pairs=10000):
# split each test document into two halves and compute topics for each half
part1 = [model[id2word_tlg.doc2bow(tokens[: len(tokens) // 2])] for tokens in test_docs]
part2 = [model[id2word_tlg.doc2bow(tokens[len(tokens) // 2 :])] for tokens in test_docs]
# print computed similarities (uses cossim)
print("average cosine similarity between corresponding parts (higher is better):")
print(np.mean([gensim.matutils.cossim(p1, p2) for p1, p2 in zip(part1, part2)]))
random_pairs = np.random.randint(0, len(test_docs), size=(num_pairs, 2))
print("average cosine similarity between {} random parts (lower is better):".format(num_pairs))
print(np.mean([gensim.matutils.cossim(part1[i[0]], part2[i[1]]) for i in random_pairs]))
In [94]:
print("LDA results:")
intra_inter(lda_model, test_docs)
LDA results:
average cosine similarity between corresponding parts (higher is better):
0.979140673252
average cosine similarity between 10000 random parts (lower is better):
0.967707618176
In [95]:
print("LSI results:")
intra_inter(lsi_model, test_docs)
LSI results:
average cosine similarity between corresponding parts (higher is better):
0.759159191756
average cosine similarity between 10000 random parts (lower is better):
0.556656437511
In [110]:
for title, tokens in iter_tlg(tlg_preprocessed):
#print(title, tokens[:10]) # print the article title and its first ten tokens
print(title)
print(lda_model[id2word_tlg.doc2bow(tokens)])
print('')
TLG2346.TXT
[(2, 0.77020063255871529), (4, 0.050162662423303819), (7, 0.17759554457537929)]
TLG1389.TXT
[(1, 0.077333330171680217), (2, 0.81018572424497037), (4, 0.029298653619360696), (7, 0.021474921514088502), (9, 0.050474519337949199)]
TLG0404.TXT
[(1, 0.02048927219191567), (2, 0.94907945485028755), (4, 0.029755487376673027)]
TLG0235.TXT
[(2, 0.9770261992926299), (4, 0.015441547453474994)]
TLG0535.TXT
[(1, 0.034813206783092389), (2, 0.71772563652669896), (3, 0.014290175511093661), (4, 0.090466392452987279), (5, 0.0652139733662397), (7, 0.018721921830686009), (9, 0.055341464745294021)]
TLG0507.TXT
[(1, 0.10259625305824292), (2, 0.41373140862637803), (4, 0.14912418250002712), (5, 0.12201859250883705), (7, 0.16176346421143439), (9, 0.050147784476549384)]
TLG1816.TXT
[(1, 0.024000415375272359), (2, 0.86327207953334895), (4, 0.028950050907302946), (5, 0.02272453752338216), (7, 0.035543720007879025), (9, 0.013945883813084248)]
TLG0476.TXT
[(1, 0.038183750715045969), (2, 0.92764021813365216), (4, 0.011431131025983235), (9, 0.02186883315968418)]
TLG0295.TXT
[(1, 0.029492800583934952), (2, 0.90198755175890344), (4, 0.019986808404588888), (5, 0.020845772484302787), (9, 0.02272792733575513)]
TLG4329.TXT
[(1, 0.020207171040531428), (2, 0.92971331210302444), (4, 0.013978082170075151), (7, 0.011355500082244162), (9, 0.011581578633356874)]
TLG1207.TXT
[(1, 0.018391857574130793), (2, 0.89752101480464819), (4, 0.030538790030887174), (5, 0.012753832587177402), (7, 0.014505065487954116), (9, 0.023993627245298948)]
TLG1409.TXT
[(1, 0.022280175045964273), (2, 0.89882033034067299), (4, 0.032669163490449106), (9, 0.027833796730770737)]
TLG0432.TXT
[(2, 0.82043708590398279), (4, 0.033710166262625151), (7, 0.068347218533548013), (9, 0.073060463782548504)]
TLG0411.TXT
[(1, 0.042884879106008848), (2, 0.90499969054629004), (4, 0.049061314604808511)]
TLG0593.TXT
[(1, 0.025606827245342519), (2, 0.74133844201359556), (4, 0.069920874155278062), (5, 0.071941985947582665), (7, 0.028196748440195915), (9, 0.060935827364016829)]
TLG0272.TXT
[(1, 0.049332658774974406), (2, 0.38717795800336335), (4, 0.1939003775181444), (5, 0.36603824446875688)]
TLG1642.TXT
[(1, 0.042193209412017138), (2, 0.58346040843898983), (4, 0.091861227469167361), (5, 0.19123202666570444), (7, 0.010006158772960815), (9, 0.078422845685976364)]
TLG1272.TXT
[(1, 0.038543464078781078), (2, 0.53786225607282323), (4, 0.043760999250244154), (5, 0.021763090892183087), (9, 0.34299528680110769)]
TLG1572.TXT
[(1, 0.028893577249211495), (2, 0.41288501589944271), (4, 0.048620033643154932), (9, 0.50409714838358499)]
TLG2565.TXT
[(2, 0.55571853106070634), (7, 0.032374534353140499), (9, 0.40412696070559906)]
TLG4326.TXT
[(1, 0.03739140609526951), (2, 0.447160205097973), (4, 0.017290842772480889), (9, 0.49284682096693105)]
TLG2475.TXT
[(0, 0.035609728475575184), (1, 0.039316971424566631), (2, 0.55809752155522696), (4, 0.035393627622755559), (7, 0.019571121621862819), (9, 0.30875789174143536)]
TLG0343.TXT
[(1, 0.061845368425033735), (2, 0.45654489460922265), (4, 0.05843666412370023), (5, 0.010423681527322043), (9, 0.40432007874348719)]
TLG2340.TXT
[(1, 0.043083795812411112), (2, 0.60306832003014099), (4, 0.0888607655116882), (9, 0.25445965434029572)]
TLG2148.TXT
[(1, 0.045762530629710002), (2, 0.46436087588911701), (4, 0.043091463203559152), (5, 0.022151440944530077), (7, 0.023610374247707139), (9, 0.39888676066676648)]
TLG1427.TXT
[(1, 0.030942123027514219), (2, 0.42597649253115016), (4, 0.059407998058555289), (5, 0.010415125613256535), (9, 0.46929534234495163)]
TLG0465.TXT
[(1, 0.01937073007160689), (2, 0.5189912415523471), (9, 0.45032772222707801)]
TLG1083.TXT
[(1, 0.060785479077584706), (2, 0.50193611358892376), (7, 0.020036532655072912), (9, 0.40790121765107246)]
TLG0666.TXT
[(1, 0.026746740103963072), (2, 0.52564285307768166), (9, 0.43546141902737351)]
TLG4235.TXT
[(1, 0.041991193440244733), (2, 0.51281294769917907), (4, 0.047353966860656772), (5, 0.02246645834242833), (9, 0.35599092047824971)]
TLG4242.TXT
[(0, 0.02268575673523205), (1, 0.045951213467644254), (2, 0.52124348088123695), (4, 0.057109439352578006), (5, 0.01581070244883782), (9, 0.33073061006057214)]
TLG2434.TXT
[(2, 0.412969938078941), (4, 0.10853198580076218), (9, 0.47235674824699841)]
TLG0237.TXT
[(0, 0.032259631700705983), (1, 0.040536764188525115), (2, 0.33612677876254055), (4, 0.025218910226698921), (5, 0.018337435250051642), (9, 0.54475142037511626)]
TLG0341.TXT
[(0, 0.015376435165460348), (1, 0.041555804049387676), (2, 0.26303260341711643), (4, 0.38907664273035447), (5, 0.026052989693869617), (9, 0.2618300782407656)]
TLG0526.TXT
[(1, 0.12015946547511074), (2, 0.52774171933388891), (4, 0.14664929955951619), (5, 0.087052876735103854), (6, 0.018980346717462519), (7, 0.01523728189724711), (9, 0.069260277891945568)]
TLG0651.TXT
[(0, 0.01142803237538356), (1, 0.075490528250820713), (2, 0.56513603571139548), (4, 0.12851671903560991), (5, 0.045884642432832329), (6, 0.016852851570842545), (7, 0.032327988586420643), (9, 0.12307258664565975)]
TLG9009.TXT
[(1, 0.085624149519186385), (2, 0.58100475142889452), (4, 0.10183810890960342), (5, 0.053566297602565878), (6, 0.01706885652791815), (7, 0.019745882472403983), (9, 0.13037828795107029)]
TLG0379.TXT
[(2, 0.48438372534384305), (5, 0.073945237882246662), (6, 0.11322761875781422), (9, 0.32022207341751091)]
TLG1281.TXT
[(1, 0.10981586779227241), (2, 0.47866460183538689), (4, 0.11050116977067992), (5, 0.065774774816754936), (6, 0.014942934617480115), (9, 0.21116474832090926)]
TLG0392.TXT
[(1, 0.056573316568680393), (2, 0.64782799749954767), (4, 0.10586541334228244), (5, 0.027234052231187085), (6, 0.018588749587282044), (7, 0.02394724022898264), (9, 0.11248399532389811)]
TLG0409.TXT
[(0, 0.1229802026497723), (1, 0.073280126980823826), (2, 0.6301386860203223), (3, 0.049582357736404153), (4, 0.076029219273962459), (9, 0.043337273901137799)]
TLG3070.TXT
[(0, 0.012142053908876736), (1, 0.083634649223502777), (2, 0.55920924865762633), (4, 0.12278267180031356), (5, 0.062748499282129264), (7, 0.015246297768108989), (9, 0.13197103402254515)]
TLG1426.TXT
[(1, 0.078263764047264428), (2, 0.60983877756203475), (4, 0.12426391765699371), (5, 0.036620576824419764), (6, 0.020959017944224518), (7, 0.018165458930182829), (9, 0.10715554522854219)]
TLG1641.TXT
[(1, 0.058123124880000293), (2, 0.64421784612907385), (4, 0.08190139523658499), (5, 0.048554573231986912), (9, 0.14860930002517242)]
TLG5040.TXT
[(1, 0.065380264207600972), (2, 0.65219806360648125), (4, 0.086542634833178533), (5, 0.045260722034459383), (6, 0.017374316400304256), (7, 0.019504504891501784), (9, 0.10333523784298365)]
TLG0545.TXT
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TLG0706.TXT
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TLG1405.TXT
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TLG1477.TXT
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TLG0096.TXT
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TLG2962.TXT
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TLG2384.TXT
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TLG0686.TXT
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TLG0068.TXT
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TLG0260.TXT
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TLG1237.TXT
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TLG0422.TXT
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TLG1132.TXT
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TLG0013.TXT
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TLG3027.TXT
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TLG0693.TXT
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TLG1583.TXT
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TLG0641.TXT
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TLG0566.TXT
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TLG4236.TXT
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TLG1923.TXT
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TLG1484.TXT
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TLG1533.TXT
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TLG0095.TXT
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TLG2058.TXT
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TLG1562.TXT
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TLG5019.TXT
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TLG0265.TXT
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TLG2121.TXT
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TLG2053.TXT
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TLG2724.TXT
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TLG2409.TXT
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TLG3047.TXT
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TLG2016.TXT
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TLG0492.TXT
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TLG4134.TXT
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TLG1661.TXT
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TLG4014.TXT
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TLG0676.TXT
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TLG9004.TXT
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TLG0503.TXT
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TLG4080.TXT
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TLG0064.TXT
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TLG1320.TXT
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TLG0577.TXT
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TLG1206.TXT
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TLG1464.TXT
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TLG2718.TXT
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TLG0358.TXT
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TLG0230.TXT
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TLG0418.TXT
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TLG0464.TXT
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TLG0746.TXT
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TLG2213.TXT
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TLG1124.TXT
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TLG4394.TXT
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TLG1599.TXT
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TLG3147.TXT
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TLG2153.TXT
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TLG0303.TXT
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TLG0488.TXT
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TLG2031.TXT
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TLG1174.TXT
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TLG0530.TXT
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TLG0231.TXT
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TLG4096.TXT
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TLG1170.TXT
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TLG1360.TXT
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TLG5016.TXT
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TLG0125.TXT
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TLG7000.TXT
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TLG4391.TXT
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TLG0610.TXT
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TLG0325.TXT
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TLG4086.TXT
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TLG0038.TXT
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TLG1332.TXT
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TLG0004.TXT
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TLG4026.TXT
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TLG4396.TXT
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TLG1622.TXT
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TLG1149.TXT
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TLG0511.TXT
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TLG2592.TXT
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TLG1334.TXT
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TLG0472.TXT
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TLG5025.TXT
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TLG0533.TXT
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TLG1431.TXT
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TLG1560.TXT
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TLG0454.TXT
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TLG2231.TXT
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TLG3140.TXT
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TLG1617.TXT
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TLG0221.TXT
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TLG0629.TXT
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TLG1491.TXT
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TLG0116.TXT
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TLG0515.TXT
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TLG9020.TXT
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TLG2702.TXT
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TLG1139.TXT
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TLG0517.TXT
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TLG3069.TXT
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TLG0554.TXT
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TLG3128.TXT
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TLG0041.TXT
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TLG3178.TXT
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TLG2734.TXT
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TLG0032.TXT
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TLG2392.TXT
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TLG1805.TXT
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TLG4019.TXT
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TLG1514.TXT
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TLG0228.TXT
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TLG1911.TXT
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DOCCAN2.TXT
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TLG0673.TXT
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TLG1232.TXT
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TLG0359.TXT
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TLG1803.TXT
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TLG4340.TXT
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TLG1516.TXT
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TLG4100.TXT
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TLG2226.TXT
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TLG4024.TXT
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TLG0327.TXT
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TLG0609.TXT
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TLG2029.TXT
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TLG0580.TXT
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TLG2027.TXT
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TLG1635.TXT
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TLG2054.TXT
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TLG2866.TXT
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TLG2738.TXT
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TLG0538.TXT
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TLG0291.TXT
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TLG0553.TXT
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TLG1577.TXT
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TLG0687.TXT
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TLG1162.TXT
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TLG2545.TXT
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TLG0480.TXT
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TLG1550.TXT
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TLG1411.TXT
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TLG0222.TXT
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TLG2046.TXT
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TLG0527.TXT
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TLG2966.TXT
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TLG4175.TXT
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TLG2193.TXT
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TLG0434.TXT
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TLG0413.TXT
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TLG2871.TXT
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TLG1738.TXT
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TLG2691.TXT
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TLG0131.TXT
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TLG1377.TXT
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TLG1636.TXT
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TLG2649.TXT
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TLG0047.TXT
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TLG0204.TXT
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TLG1728.TXT
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TLG2062.TXT
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TLG1197.TXT
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TLG2232.TXT
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TLG2162.TXT
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TLG5024.TXT
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TLG1709.TXT
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TLG1756.TXT
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TLG4344.TXT
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TLG2630.TXT
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TLG1365.TXT
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TLG0498.TXT
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TLG1322.TXT
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TLG3125.TXT
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TLG0718.TXT
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TLG0752.TXT
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TLG0276.TXT
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TLG9018.TXT
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TLG1275.TXT
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TLG1308.TXT
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TLG4161.TXT
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TLG1953.TXT
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TLG0648.TXT
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TLG2237.TXT
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TLG0065.TXT
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TLG0572.TXT
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TLG1527.TXT
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TLG0207.TXT
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TLG1461.TXT
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TLG0089.TXT
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TLG4018.TXT
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TLG2573.TXT
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TLG3023.TXT
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TLG4003.TXT
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TLG1205.TXT
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TLG0009.TXT
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TLG1557.TXT
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TLG0474.TXT
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TLG0627.TXT
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TLG3173.TXT
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TLG4157.TXT
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TLG3018.TXT
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TLG2391.TXT
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TLG1276.TXT
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TLG0053.TXT
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TLG0293.TXT
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TLG1602.TXT
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TLG1524.TXT
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TLG1553.TXT
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TLG1804.TXT
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TLG4346.TXT
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TLG0743.TXT
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TLG0092.TXT
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TLG1263.TXT
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TLG0726.TXT
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TLG0317.TXT
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TLG1469.TXT
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TLG1413.TXT
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TLG0738.TXT
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TLG1811.TXT
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TLG2017.TXT
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TLG3159.TXT
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TLG1666.TXT
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TLG2201.TXT
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TLG2807.TXT
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TLG0253.TXT
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TLG1194.TXT
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TLG4302.TXT
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TLG0703.TXT
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TLG2278.TXT
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TLG2937.TXT
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TLG3182.TXT
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TLG0698.TXT
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TLG1138.TXT
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TLG1310.TXT
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TLG4173.TXT
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TLG3170.TXT
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TLG2190.TXT
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TLG2583.TXT
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TLG1245.TXT
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TLG2532.TXT
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TLG1287.TXT
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TLG0615.TXT
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TLG0725.TXT
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TLG5015.TXT
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TLG2482.TXT
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TLG2021.TXT
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TLG0504.TXT
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TLG0727.TXT
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TLG4034.TXT
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TLG0306.TXT
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TLG2331.TXT
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TLG1639.TXT
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TLG2461.TXT
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TLG2341.TXT
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TLG2122.TXT
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TLG3079.TXT
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TLG2995.TXT
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TLG1118.TXT
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TLG1450.TXT
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TLG1570.TXT
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TLG2512.TXT
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TLG5032.TXT
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TLG0544.TXT
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TLG3177.TXT
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TLG4076.TXT
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TLG1347.TXT
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TLG4170.TXT
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TLG0037.TXT
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TLG1199.TXT
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TLG4397.TXT
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TLG2274.TXT
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TLG0471.TXT
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TLG4287.TXT
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TLG0255.TXT
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TLG1534.TXT
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TLG0468.TXT
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TLG1200.TXT
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TLG4328.TXT
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TLG2903.TXT
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TLG0328.TXT
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TLG0452.TXT
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TLG2654.TXT
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TLG0020.TXT
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TLG0083.TXT
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TLG0421.TXT
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TLG2721.TXT
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TLG2949.TXT
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TLG1353.TXT
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TLG3014.TXT
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TLG1230.TXT
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TLG2934.TXT
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TLG4291.TXT
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TLG2218.TXT
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TLG3115.TXT
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TLG0556.TXT
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TLG2594.TXT
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TLG2797.TXT
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TLG4029.TXT
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TLG0214.TXT
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TLG0203.TXT
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TLG1858.TXT
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TLG0246.TXT
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TLG1193.TXT
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TLG0662.TXT
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TLG4126.TXT
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TLG2011.TXT
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TLG2386.TXT
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TLG2449.TXT
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TLG0542.TXT
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TLG0633.TXT
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TLG0043.TXT
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TLG1175.TXT
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TLG0027.TXT
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TLG2770.TXT
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TLG0045.TXT
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TLG4030.TXT
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TLG1828.TXT
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TLG4037.TXT
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TLG1408.TXT
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TLG2300.TXT
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TLG0137.TXT
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TLG1715.TXT
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TLG0501.TXT
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TLG1182.TXT
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TLG1918.TXT
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TLG0439.TXT
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TLG0605.TXT
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TLG2289.TXT
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TLG3168.TXT
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TLG1105.TXT
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TLG0736.TXT
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TLG1145.TXT
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TLG1724.TXT
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TLG1131.TXT
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TLG9023.TXT
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TLG2525.TXT
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TLG0435.TXT
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TLG1346.TXT
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TLG0087.TXT
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TLG3136.TXT
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TLG0537.TXT
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TLG0731.TXT
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TLG2216.TXT
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TLG1590.TXT
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TLG0216.TXT
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TLG3051.TXT
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TLG0006.TXT
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TLG0550.TXT
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TLG0034.TXT
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TLG1685.TXT
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TLG1419.TXT
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TLG0301.TXT
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TLG1181.TXT
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TLG0463.TXT
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TLG2371.TXT
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TLG1485.TXT
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TLG2396.TXT
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TLG0652.TXT
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TLG0897.TXT
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TLG1489.TXT
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TLG0402.TXT
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TLG0489.TXT
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TLG2192.TXT
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TLG2110.TXT
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TLG2239.TXT
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TLG4015.TXT
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TLG1273.TXT
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TLG0433.TXT
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TLG0617.TXT
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TLG1180.TXT
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TLG0084.TXT
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TLG9007.TXT
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TLG4149.TXT
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TLG5022.TXT
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TLG0002.TXT
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TLG1167.TXT
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TLG2412.TXT
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TLG1128.TXT
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TLG5030.TXT
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TLG1135.TXT
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TLG1595.TXT
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TLG2367.TXT
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TLG4090.TXT
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TLG1383.TXT
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TLG0721.TXT
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TLG9008.TXT
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TLG1508.TXT
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TLG0532.TXT
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TLG4117.TXT
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TLG1667.TXT
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TLG1530.TXT
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TLG0082.TXT
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TLG0495.TXT
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TLG2444.TXT
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TLG1605.TXT
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TLG1727.TXT
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TLG1821.TXT
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TLG2587.TXT
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TLG2946.TXT
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TLG0494.TXT
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TLG1016.TXT
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TLG1133.TXT
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TLG5026.TXT
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TLG1648.TXT
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TLG4238.TXT
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TLG0186.TXT
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TLG0561.TXT
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TLG2577.TXT
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TLG0484.TXT
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TLG1367.TXT
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TLG0458.TXT
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TLG4091.TXT
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TLG0597.TXT
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TLG0266.TXT
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TLG1698.TXT
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TLG0081.TXT
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TLG0062.TXT
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TLG2345.TXT
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TLG1626.TXT
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TLG1542.TXT
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TLG1282.TXT
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TLG1977.TXT
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TLG2291.TXT
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TLG1799.TXT
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TLG4094.TXT
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TLG1278.TXT
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TLG0400.TXT
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TLG1637.TXT
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TLG2169.TXT
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TLG4184.TXT
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TLG1387.TXT
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TLG0661.TXT
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TLG2157.TXT
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TLG2043.TXT
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TLG2322.TXT
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TLG1992.TXT
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TLG5037.TXT
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TLG0079.TXT
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TLG2030.TXT
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TLG3100.TXT
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TLG5033.TXT
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TLG2317.TXT
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TLG0286.TXT
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TLG4016.TXT
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TLG0090.TXT
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TLG4124.TXT
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TLG1392.TXT
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TLG0613.TXT
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TLG2424.TXT
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TLG1600.TXT
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TLG0499.TXT
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TLG0319.TXT
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TLG0717.TXT
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TLG0077.TXT
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TLG5029.TXT
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TLG0011.TXT
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TLG5009.TXT
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TLG1224.TXT
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TLG1917.TXT
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TLG0644.TXT
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TLG0401.TXT
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TLG2182.TXT
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TLG2059.TXT
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TLG1177.TXT
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TLG3188.TXT
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TLG4021.TXT
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TLG2001.TXT
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TLG0549.TXT
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TLG2695.TXT
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TLG1688.TXT
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TLG0584.TXT
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TLG5003.TXT
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TLG1445.TXT
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TLG0072.TXT
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TLG0620.TXT
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TLG2970.TXT
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TLG1161.TXT
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TLG1288.TXT
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TLG1004.TXT
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TLG0017.TXT
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TLG4324.TXT
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TLG1512.TXT
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TLG0015.TXT
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TLG0031.TXT
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TLG1323.TXT
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TLG1286.TXT
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TLG0042.TXT
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TLG4301.TXT
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TLG1487.TXT
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TLG1190.TXT
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TLG2560.TXT
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TLG2601.TXT
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TLG0744.TXT
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TLG1321.TXT
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TLG1696.TXT
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TLG2009.TXT
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TLG0579.TXT
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TLG1362.TXT
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TLG2305.TXT
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TLG2657.TXT
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TLG4089.TXT
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TLG0690.TXT
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TLG2118.TXT
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TLG1225.TXT
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TLG2158.TXT
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TLG1452.TXT
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TLG4393.TXT
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TLG0446.TXT
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TLG2458.TXT
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TLG1364.TXT
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TLG2387.TXT
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TLG1262.TXT
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TLG3088.TXT
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TLG1271.TXT
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TLG1296.TXT
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TLG3156.TXT
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TLG0564.TXT
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TLG0713.TXT
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TLG0714.TXT
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TLG0671.TXT
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TLG0280.TXT
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TLG0093.TXT
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TLG1441.TXT
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TLG2116.TXT
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TLG1134.TXT
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TLG5035.TXT
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TLG5000.TXT
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TLG1443.TXT
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TLG4331.TXT
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TLG1646.TXT
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TLG0482.TXT
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TLG1379.TXT
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TLG1703.TXT
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TLG1158.TXT
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TLG1125.TXT
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TLG2189.TXT
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TLG1969.TXT
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TLG0569.TXT
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TLG2945.TXT
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TLG1466.TXT
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TLG2248.TXT
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TLG2950.TXT
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TLG2039.TXT
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TLG9022.TXT
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TLG3139.TXT
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TLG0069.TXT
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TLG1695.TXT
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TLG1244.TXT
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TLG1679.TXT
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TLG1725.TXT
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TLG2904.TXT
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TLG1596.TXT
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TLG0729.TXT
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TLG4097.TXT
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TLG1375.TXT
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TLG1638.TXT
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TLG4303.TXT
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TLG0668.TXT
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TLG0406.TXT
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TLG2334.TXT
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TLG0245.TXT
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TLG0512.TXT
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TLG0141.TXT
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TLG4066.TXT
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TLG2036.TXT
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TLG1552.TXT
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TLG1231.TXT
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TLG2542.TXT
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TLG1366.TXT
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TLG1608.TXT
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TLG0443.TXT
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TLG2596.TXT
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TLG4061.TXT
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TLG2265.TXT
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TLG1188.TXT
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TLG0048.TXT
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TLG0656.TXT
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TLG0440.TXT
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TLG1397.TXT
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TLG0539.TXT
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TLG0660.TXT
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TLG1121.TXT
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TLG2014.TXT
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TLG1313.TXT
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TLG4304.TXT
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TLG2203.TXT
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TLG0562.TXT
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TLG2703.TXT
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TLG1710.TXT
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TLG1558.TXT
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TLG2604.TXT
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TLG0510.TXT
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TLG1470.TXT
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TLG0019.TXT
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TLG5034.TXT
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TLG2140.TXT
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TLG4036.TXT
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TLG0024.TXT
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TLG2151.TXT
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TLG0525.TXT
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TLG2008.TXT
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TLG3064.TXT
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TLG0657.TXT
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TLG2032.TXT
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TLG2141.TXT
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TLG1256.TXT
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TLG2055.TXT
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TLG2397.TXT
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TLG1763.TXT
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TLG0663.TXT
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TLG0628.TXT
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TLG0715.TXT
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TLG1451.TXT
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TLG1270.TXT
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TLG1147.TXT
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TLG0521.TXT
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TLG0339.TXT
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TLG0647.TXT
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TLG3169.TXT
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TLG2047.TXT
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TLG1222.TXT
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TLG0309.TXT
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TLG0570.TXT
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TLG1867.TXT
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TLG1752.TXT
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TLG1021.TXT
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TLG4166.TXT
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TLG0658.TXT
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TLG0334.TXT
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TLG1475.TXT
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TLG1687.TXT
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TLG2202.TXT
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TLG1488.TXT
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TLG2571.TXT
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TLG3043.TXT
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TLG2235.TXT
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TLG5031.TXT
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TLG2339.TXT
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TLG1319.TXT
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TLG0023.TXT
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TLG4150.TXT
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TLG0405.TXT
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TLG4046.TXT
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TLG4311.TXT
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TLG4039.TXT
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TLG0455.TXT
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TLG1390.TXT
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TLG2894.TXT
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TLG0451.TXT
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TLG2196.TXT
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TLG1691.TXT
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TLG0298.TXT
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TLG1155.TXT
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TLG3075.TXT
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TLG1402.TXT
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TLG3120.TXT
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TLG0740.TXT
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TLG0543.TXT
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TLG1352.TXT
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TLG1289.TXT
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TLG1302.TXT
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TLG1567.TXT
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TLG0067.TXT
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TLG0541.TXT
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TLG2948.TXT
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TLG2742.TXT
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TLG1183.TXT
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TLG1544.TXT
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TLG0445.TXT
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TLG0318.TXT
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TLG1503.TXT
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TLG0742.TXT
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TLG2049.TXT
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TLG2655.TXT
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TLG0490.TXT
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TLG2210.TXT
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TLG1479.TXT
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TLG0290.TXT
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TLG0716.TXT
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TLG0528.TXT
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TLG0431.TXT
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TLG1189.TXT
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TLG2042.TXT
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TLG0653.TXT
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TLG1251.TXT
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TLG2178.TXT
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TLG2956.TXT
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TLG1734.TXT
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TLG1759.TXT
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TLG4038.TXT
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TLG1551.TXT
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TLG4374.TXT
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TLG4101.TXT
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TLG5018.TXT
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TLG1249.TXT
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TLG2022.TXT
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TLG1406.TXT
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TLG0098.TXT
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TLG1750.TXT
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TLG1339.TXT
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TLG1373.TXT
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TLG5048.TXT
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TLG0275.TXT
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TLG2019.TXT
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TLG1693.TXT
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TLG2591.TXT
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TLG2270.TXT
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TLG1699.TXT
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TLG1432.TXT
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TLG1430.TXT
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TLG0086.TXT
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TLG0505.TXT
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TLG2255.TXT
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TLG2333.TXT
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TLG2798.TXT
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TLG0888.TXT
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TLG4083.TXT
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TLG0547.TXT
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TLG0407.TXT
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TLG0419.TXT
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TLG0060.TXT
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TLG1941.TXT
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TLG1344.TXT
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TLG1521.TXT
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TLG0524.TXT
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TLG1494.TXT
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TLG1584.TXT
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TLG1729.TXT
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TLG2816.TXT
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TLG4172.TXT
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TLG1499.TXT
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TLG0026.TXT
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TLG2194.TXT
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TLG1704.TXT
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TLG4105.TXT
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TLG0624.TXT
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TLG0360.TXT
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TLG1259.TXT
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TLG0057.TXT
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TLG2004.TXT
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TLG1201.TXT
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TLG1173.TXT
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TLG2294.TXT
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TLG2245.TXT
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TLG1338.TXT
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TLG1817.TXT
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TLG0202.TXT
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TLG1765.TXT
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TLG2632.TXT
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TLG1814.TXT
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TLG1649.TXT
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TLG0012.TXT
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TLG0363.TXT
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TLG0264.TXT
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TLG0001.TXT
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TLG1306.TXT
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TLG0483.TXT
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TLG0030.TXT
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TLG4040.TXT
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TLG0277.TXT
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TLG1400.TXT
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TLG4020.TXT
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TLG1350.TXT
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TLG1191.TXT
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TLG0028.TXT
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TLG0294.TXT
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TLG1146.TXT
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TLG2914.TXT
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TLG0745.TXT
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TLG1712.TXT
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TLG0574.TXT
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TLG2602.TXT
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TLG0361.TXT
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TLG2050.TXT
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TLG1407.TXT
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TLG0607.TXT
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TLG0623.TXT
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TLG0208.TXT
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TLG4082.TXT
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TLG2625.TXT
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TLG2326.TXT
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TLG4339.TXT
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TLG1832.TXT
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TLG0568.TXT
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TLG0591.TXT
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TLG0447.TXT
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TLG4017.TXT
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TLG0259.TXT
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TLG2225.TXT
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TLG0297.TXT
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TLG2762.TXT
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TLG2304.TXT
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TLG0238.TXT
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TLG0199.TXT
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TLG1210.TXT
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TLG0594.TXT
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TLG0578.TXT
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TLG2284.TXT
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TLG0531.TXT
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TLG0205.TXT
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TLG0635.TXT
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TLG2680.TXT
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TLG2109.TXT
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TLG0448.TXT
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TLG3094.TXT
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TLG0750.TXT
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TLG0058.TXT
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TLG1298.TXT
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TLG5010.TXT
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TLG1428.TXT
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TLG0016.TXT
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TLG0555.TXT
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TLG1247.TXT
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TLG1307.TXT
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TLG1976.TXT
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TLG2111.TXT
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TLG1163.TXT
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TLG0201.TXT
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TLG0097.TXT
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TLG1208.TXT
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TLG0500.TXT
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TLG1318.TXT
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TLG1328.TXT
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TLG1569.TXT
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TLG2566.TXT
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TLG4237.TXT
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TLG2133.TXT
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TLG1772.TXT
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TLG1482.TXT
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TLG5017.TXT
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TLG2354.TXT
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TLG0040.TXT
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TLG4032.TXT
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TLG0243.TXT
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TLG1252.TXT
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TLG2115.TXT
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TLG2040.TXT
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TLG1771.TXT
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TLG0845.TXT
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TLG2246.TXT
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TLG4294.TXT
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TLG2195.TXT
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TLG1705.TXT
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TLG2595.TXT
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TLG1650.TXT
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TLG0728.TXT
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TLG0029.TXT
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TLG0429.TXT
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TLG0377.TXT
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TLG1498.TXT
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TLG5023.TXT
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TLG1459.TXT
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TLG1152.TXT
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TLG1545.TXT
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TLG2701.TXT
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TLG0307.TXT
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TLG2426.TXT
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TLG2074.TXT
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TLG2041.TXT
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TLG2172.TXT
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TLG1123.TXT
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TLG1692.TXT
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TLG1548.TXT
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TLG1700.TXT
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TLG0654.TXT
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TLG5045.TXT
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TLG0697.TXT
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TLG0481.TXT
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TLG0436.TXT
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TLG0691.TXT
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TLG1435.TXT
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TLG1574.TXT
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TLG2877.TXT
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TLG2112.TXT
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TLG0337.TXT
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TLG0267.TXT
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TLG1305.TXT
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TLG0417.TXT
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TLG4288.TXT
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TLG0486.TXT
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TLG2511.TXT
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TLG1203.TXT
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TLG1164.TXT
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TLG1268.TXT
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TLG1250.TXT
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TLG0200.TXT
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TLG0219.TXT
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TLG4292.TXT
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TLG0571.TXT
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TLG0460.TXT
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TLG4167.TXT
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TLG0473.TXT
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TLG4165.TXT
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TLG1615.TXT
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TLG0215.TXT
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TLG1254.TXT
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TLG1342.TXT
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TLG1791.TXT
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TLG2002.TXT
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TLG1324.TXT
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TLG2127.TXT
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TLG1355.TXT
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TLG4099.TXT
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TLG1749.TXT
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TLG0646.TXT
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TLG1312.TXT
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TLG2766.TXT
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TLG1720.TXT
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TLG2881.TXT
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TLG1645.TXT
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TLG1264.TXT
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TLG1439.TXT
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TLG0630.TXT
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TLG2679.TXT
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TLG4318.TXT
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TLG5004.TXT
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TLG4227.TXT
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TLG3185.TXT
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TLG1907.TXT
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TLG3127.TXT
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TLG1506.TXT
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TLG2051.TXT
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TLG4332.TXT
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TLG1813.TXT
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TLG1447.TXT
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TLG0049.TXT
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TLG0598.TXT
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TLG1421.TXT
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TLG0036.TXT
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TLG5039.TXT
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TLG1325.TXT
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TLG0410.TXT
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TLG0719.TXT
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TLG1242.TXT
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TLG1585.TXT
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TLG0509.TXT
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TLG1681.TXT
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TLG4115.TXT
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TLG0551.TXT
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TLG4102.TXT
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TLG2033.TXT
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TLG1341.TXT
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TLG1143.TXT
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TLG2181.TXT
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TLG1258.TXT
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TLG1248.TXT
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TLG0487.TXT
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TLG0423.TXT
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TLG1171.TXT
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TLG1555.TXT
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TLG0051.TXT
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TLG0050.TXT
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TLG2185.TXT
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TLG2260.TXT
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TLG1274.TXT
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TLG4084.TXT
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TLG4098.TXT
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TLG0351.TXT
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TLG0456.TXT
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TLG1165.TXT
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TLG0357.TXT
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TLG2800.TXT
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TLG4000.TXT
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TLG9021.TXT
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TLG3130.TXT
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TLG1588.TXT
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TLG0502.TXT
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TLG0695.TXT
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TLG0637.TXT
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TLG1412.TXT
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TLG0563.TXT
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TLG3141.TXT
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TLG3145.TXT
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TLG3144.TXT
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TLG2744.TXT
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TLG0252.TXT
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TLG0733.TXT
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TLG1340.TXT
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TLG1507.TXT
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TLG1753.TXT
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TLG1213.TXT
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TLG1766.TXT
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TLG2806.TXT
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TLG3086.TXT
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TLG0618.TXT
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TLG4174.TXT
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TLG1578.TXT
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TLG0582.TXT
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TLG1382.TXT
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TLG4286.TXT
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TLG1471.TXT
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TLG0643.TXT
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TLG2025.TXT
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TLG0508.TXT
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TLG4289.TXT
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TLG0529.TXT
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TLG0055.TXT
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TLG0592.TXT
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TLG1277.TXT
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TLG1297.TXT
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TLG1754.TXT
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TLG1535.TXT
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TLG0649.TXT
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TLG1609.TXT
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TLG1559.TXT
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TLG0491.TXT
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TLG0388.TXT
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TLG0475.TXT
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TLG1571.TXT
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TLG1186.TXT
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TLG2298.TXT
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TLG1801.TXT
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TLG1737.TXT
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TLG0540.TXT
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TLG2460.TXT
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TLG4110.TXT
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TLG1611.TXT
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TLG3187.TXT
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TLG0565.TXT
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TLG1326.TXT
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TLG0213.TXT
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TLG0546.TXT
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TLG0233.TXT
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TLG0330.TXT
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TLG2060.TXT
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TLG1815.TXT
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TLG1838.TXT
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TLG4001.TXT
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TLG2000.TXT
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TLG1682.TXT
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TLG0251.TXT
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TLG2580.TXT
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TLG0005.TXT
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TLG0218.TXT
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TLG1665.TXT
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TLG1316.TXT
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TLG0165.TXT
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TLG4337.TXT
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TLG1372.TXT
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TLG2063.TXT
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TLG1486.TXT
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TLG4093.TXT
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TLG1129.TXT
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TLG2648.TXT
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TLG1678.TXT
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TLG3039.TXT
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TLG2035.TXT
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TLG0602.TXT
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TLG1358.TXT
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TLG1733.TXT
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TLG4330.TXT
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TLG1369.TXT
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TLG1429.TXT
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TLG4193.TXT
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TLG0007.TXT
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TLG0039.TXT
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TLG2969.TXT
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TLG1751.TXT
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TLG1219.TXT
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TLG2228.TXT
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TLG0383.TXT
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TLG1701.TXT
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TLG0748.TXT
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TLG2012.TXT
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TLG1632.TXT
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TLG1747.TXT
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TLG0063.TXT
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TLG2034.TXT
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TLG1240.TXT
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TLG0075.TXT
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TLG1523.TXT
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TLG1606.TXT
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TLG0730.TXT
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TLG0444.TXT
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TLG2061.TXT
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TLG0340.TXT
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TLG0384.TXT
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TLG2586.TXT
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TLG3142.TXT
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TLG0033.TXT
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TLG0612.TXT
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TLG0679.TXT
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TLG0390.TXT
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TLG3040.TXT
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TLG0437.TXT
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TLG4072.TXT
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TLG0373.TXT
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TLG1226.TXT
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TLG0734.TXT
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TLG0688.TXT
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TLG1266.TXT
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TLG2548.TXT
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TLG4171.TXT
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TLG1483.TXT
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TLG1881.TXT
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TLG2330.TXT
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TLG2010.TXT
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TLG1664.TXT
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TLG2404.TXT
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TLG3155.TXT
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TLG1291.TXT
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TLG0450.TXT
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TLG0198.TXT
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TLG1368.TXT
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TLG4075.TXT
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TLG0614.TXT
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TLG0014.TXT
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TLG3181.TXT
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TLG1168.TXT
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TLG7051.TXT
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TLG4338.TXT
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TLG2168.TXT
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TLG0380.TXT
[(1, 0.072975687879785853), (2, 0.58308844162238083), (4, 0.082359468612125453), (5, 0.077416451032025033), (6, 0.02472596919966729), (7, 0.052928094238478232), (8, 0.011389277737998903), (9, 0.09467597285362471)]
TLG0066.TXT
[(1, 0.070926759881074219), (2, 0.62233609085542696), (4, 0.10217816938306894), (5, 0.051874477453048795), (6, 0.012100012768809676), (7, 0.019524990447554239), (9, 0.11401660424980495)]
TLG0059.TXT
[(1, 0.075790106688360448), (2, 0.65948977646423912), (4, 0.090263533709317556), (5, 0.034858488288312332), (7, 0.0145737598154837), (9, 0.11353133615715078)]
TLG0212.TXT
[(0, 0.022282549575223371), (1, 0.039478223057102843), (2, 0.63826384444213469), (4, 0.10794965716542737), (5, 0.060338043201943754), (6, 0.020192461991333309), (7, 0.01242202907496333), (9, 0.098847662893280716)]
TLG1896.TXT
[(1, 0.083845215332726569), (2, 0.56347066063475848), (4, 0.088455320437133), (5, 0.047121055914875171), (7, 0.012508141610029109), (9, 0.20347577951339654)]
TLG0583.TXT
[(1, 0.072435807262158383), (2, 0.67935435977458336), (4, 0.062913992718130293), (5, 0.034804761362963794), (7, 0.02344790919381113), (9, 0.11107679033358542)]
TLG0304.TXT
[(1, 0.084966376606775043), (2, 0.59995815830970178), (4, 0.074847320694781172), (5, 0.049469072239403764), (6, 0.023289486238389629), (9, 0.15614286139492692)]
TLG4395.TXT
[(1, 0.095382088252684988), (2, 0.65041535361456859), (4, 0.10790089985061069), (5, 0.11275342971682215), (9, 0.026697463350951189)]
TLG0414.TXT
[(1, 0.2898776744908183), (2, 0.55438758166742008), (6, 0.143867327126697)]
TLG0497.TXT
[(1, 0.10550558857721228), (2, 0.59917164111623278), (4, 0.13607541756475564), (5, 0.031808508205979739), (7, 0.01519722346779934), (9, 0.10124520015562793)]
TLG0626.TXT
[(1, 0.056275252008287896), (2, 0.64537915916546695), (4, 0.078264281980374828), (5, 0.038401650094285499), (7, 0.02153553341856242), (9, 0.1502799066007148)]
TLG0389.TXT
[(1, 0.068815465864175418), (2, 0.66344219015930517), (4, 0.11039752013600002), (5, 0.037276695891993034), (9, 0.10510290623507951)]
TLG5014.TXT
[(1, 0.068377191711427004), (2, 0.61440014609017712), (4, 0.089730591523633621), (5, 0.042288835938057781), (6, 0.011937286965351073), (7, 0.014057639229081599), (9, 0.1510055377949297)]
TLG0261.TXT
[(1, 0.072415019471785066), (2, 0.55353263210067349), (4, 0.078851831982294271), (5, 0.068432032194836442), (6, 0.020728868126518474), (7, 0.035778696626412608), (9, 0.16434021834135185)]
TLG4031.TXT
[(1, 0.057358836144419292), (2, 0.65980850814712888), (4, 0.07608886886063386), (5, 0.032272202709618718), (7, 0.014572341400961312), (9, 0.14867490932385447)]
TLG2236.TXT
[(0, 0.020535538068296444), (1, 0.1060262762466356), (2, 0.54674429803688085), (4, 0.11872852671997017), (5, 0.071532554779376797), (6, 0.013265892312137576), (7, 0.016897644740187484), (8, 0.011184265159125536), (9, 0.091690587688604594)]
TLG0645.TXT
[(1, 0.067475352419037404), (2, 0.64188913629306343), (4, 0.10566303176137357), (5, 0.039790671875924465), (7, 0.010743944659654669), (9, 0.12268362187693496)]
TLG0018.TXT
[(1, 0.083849415384068718), (2, 0.59383704692975325), (4, 0.10464665740674987), (5, 0.051137567863266467), (6, 0.012859724117643606), (7, 0.01482308130613043), (9, 0.12936890746757745)]
TLG1376.TXT
[(1, 0.075394254017858486), (2, 0.59384867809876829), (4, 0.074223168445029103), (5, 0.074636518199209481), (7, 0.029635213262642922), (9, 0.14352763396545859)]
TLG1354.TXT
[(1, 0.13878047253679845), (2, 0.53681850042778767), (4, 0.19506012598289241), (5, 0.017662338412764409), (6, 0.017077007093564765), (7, 0.011822737437903338), (9, 0.074171974463838344)]
TLG0071.TXT
[(1, 0.065351429190173407), (2, 0.55862016726867081), (4, 0.18314970390938382), (5, 0.04192327333446224), (7, 0.013311100798031935), (9, 0.12801792777998316)]
TLG2003.TXT
[(1, 0.085117940394915881), (2, 0.60588591771051015), (4, 0.11577825873030605), (5, 0.048432046724955061), (7, 0.012623065535023485), (9, 0.11409720836324481)]
TLG2960.TXT
[(0, 0.017702223371765125), (1, 0.067832992865215574), (2, 0.61063374863795994), (4, 0.076275345123557242), (5, 0.056823733056910873), (6, 0.016088367110594817), (7, 0.025885955741976767), (9, 0.12059630657399871)]
TLG1436.TXT
[(0, 0.030205090413528309), (1, 0.047954647373120843), (2, 0.67387158925811064), (4, 0.1050796145095089), (5, 0.04055334676198738), (7, 0.019243512107910785), (9, 0.078558940691324519)]
TLG2006.TXT
[(1, 0.081154332448805877), (2, 0.60147670681975174), (4, 0.10501068894466456), (5, 0.051872662787498434), (6, 0.014758723932610658), (7, 0.011453569403154501), (9, 0.12529998097279954)]
TLG2456.TXT
[(1, 0.11376602583514285), (2, 0.80286290647680436), (6, 0.070160364723707097)]
TLG0425.TXT
[(1, 0.10072152222285331), (2, 0.65961933958768004), (4, 0.10465230626922913), (9, 0.1188267351457087)]
TLG3176.TXT
[(1, 0.080801775713455773), (2, 0.59226711996920467), (4, 0.1135027393948624), (5, 0.041787166278231321), (7, 0.018649246172510819), (9, 0.13486663510325264)]
TLG1890.TXT
[(0, 0.019652578576138592), (1, 0.089098955264998933), (2, 0.50854775537611485), (4, 0.13196252674265133), (5, 0.022740618924063028), (6, 0.013742331894993697), (7, 0.013474108597846549), (9, 0.19981013832346017)]
TLG0655.TXT
[(1, 0.10967752728163584), (2, 0.53978781802814668), (4, 0.1381980901889967), (5, 0.056525694773535411), (7, 0.026814734491300452), (9, 0.10991829204447004)]
TLG0074.TXT
[(1, 0.076415658206676923), (2, 0.53168302520019717), (4, 0.14962488553722542), (5, 0.063648734451868291), (6, 0.012277378087714015), (7, 0.015543723502355352), (9, 0.13999644012953491)]
TLG5038.TXT
[(1, 0.065379424780807313), (2, 0.60823924488267855), (4, 0.099385803889008723), (5, 0.043861094896396273), (7, 0.01782560468702735), (9, 0.14758848969635463)]
TLG2552.TXT
[(0, 0.021583352419610187), (1, 0.043234864274061181), (2, 0.54182002723511147), (4, 0.093623963743659555), (5, 0.10758397837493734), (6, 0.011351161674408347), (7, 0.026734649678640556), (9, 0.15276914291956942)]
TLG2589.TXT
[(1, 0.047924011064500539), (2, 0.67226434268475743), (4, 0.094260654224275264), (5, 0.028569782243213094), (7, 0.019403010565574648), (9, 0.12881053911827861)]
TLG1303.TXT
[(1, 0.06492949143610631), (2, 0.644524781107641), (3, 0.010474767319953006), (4, 0.07614344189022848), (5, 0.1248161027540972), (7, 0.01109688172500815), (9, 0.066675013695490601)]
TLG2303.TXT
[(1, 0.063838887193654034), (2, 0.68700459559897453), (4, 0.050275436571284825), (5, 0.039636707071468859), (7, 0.045854924942459324), (9, 0.1127388797184853)]
TLG1391.TXT
[(2, 0.71102078151154702), (4, 0.067302531136089402), (6, 0.064112574607820549), (7, 0.022727418324143892), (9, 0.12957152133585073)]
TLG0667.TXT
[(1, 0.071879284246646052), (2, 0.56085946989559965), (4, 0.13230818134473027), (5, 0.048806551550259543), (7, 0.012383229357287421), (9, 0.15914653957032171)]
TLG2234.TXT
[(1, 0.054792106588751333), (2, 0.69200183277848326), (3, 0.010607704537414027), (4, 0.054936694047964453), (5, 0.015426430097629921), (7, 0.044205549756484844), (9, 0.12728512214521343)]
TLG1154.TXT
[(1, 0.049280799564710431), (2, 0.69788726185815397), (4, 0.099524416762410764), (5, 0.031568388912193546), (6, 0.011427377274732071), (7, 0.012276179800421184), (9, 0.096658484197469779)]
TLG1239.TXT
[(1, 0.10893944313652597), (2, 0.57923629590984926), (4, 0.09873794062340846), (5, 0.031528832116559609), (6, 0.018516724253440919), (9, 0.16218761465158016)]
TLG0426.TXT
[(0, 0.046465515705487292), (1, 0.029304830270493576), (2, 0.51677753772057811), (4, 0.25265824479334276), (9, 0.15191965047829675)]
TLG1735.TXT
[(1, 0.076906592009592892), (2, 0.54729716966053654), (4, 0.092654816098005091), (5, 0.080528673519588798), (6, 0.021047155450376335), (9, 0.18099443619071351)]
TLG2023.TXT
[(1, 0.059913382847830786), (2, 0.62176741291080373), (4, 0.092582038946801531), (5, 0.040220682420336662), (6, 0.010833881689896536), (7, 0.015363510157864628), (9, 0.15330120814558604)]
TLG2506.TXT
[(1, 0.13023901175386599), (2, 0.37525000416894827), (4, 0.084523205047392622), (5, 0.35547150150978779), (9, 0.051429182400569991)]
TLG1127.TXT
[(1, 0.072944521435799284), (2, 0.56993100556749587), (4, 0.034782337426070599), (5, 0.03492663204818381), (6, 0.033670854086981464), (7, 0.020608134726592696), (9, 0.2330145871700316)]
TLG1736.TXT
[(1, 0.03044925772921752), (2, 0.586298357591303), (4, 0.1453162719668486), (7, 0.046253071192684514), (9, 0.1849243996256078)]
TLG3002.TXT
[(1, 0.062662232750699418), (2, 0.59669790829531633), (4, 0.072784786312179134), (5, 0.038745265938923953), (9, 0.21860384474020761)]
TLG4139.TXT
[(1, 0.074700155265828586), (2, 0.61731319376938032), (4, 0.10174765415046903), (5, 0.043470707346181151), (7, 0.010429989251275539), (9, 0.13576910670161665)]
TLG0003.TXT
[(1, 0.091352987065495703), (2, 0.58025042125791748), (4, 0.11874288604802567), (5, 0.053947074377210989), (6, 0.01192766806319099), (7, 0.017500635867457079), (9, 0.11973188542533245)]
Following: http://nbviewer.jupyter.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb
In [112]:
lda_model.show_topics()
Out[112]:
[(0,
'0.001*"διαφωνει" + 0.001*"διακεκαυμενη" + 0.000*"εναλλαγαι" + 0.000*"θερινη" + 0.000*"βουλομεθα" + 0.000*"οικετων" + 0.000*"καιροι" + 0.000*"λυσαωνθειηδεση" + 0.000*"ωργισμενους" + 0.000*"δεομενω"'),
(1,
'0.002*"νενευκασιν" + 0.001*"αδωμος" + 0.001*"μυουμενους" + 0.001*"νοτιωτερα" + 0.001*"διακειμενων" + 0.001*"αιστωτηριον" + 0.001*"προειρημενον" + 0.001*"επιταξ" + 0.001*"δηλουντος" + 0.001*"ασπετον"'),
(2,
'0.001*"αγνοιαν" + 0.001*"ακουσας" + 0.001*"ακρατον" + 0.001*"κινδυνευοντι" + 0.001*"λαμπτηρα" + 0.001*"εγενετο" + 0.000*"στοιχους" + 0.000*"ερωτος" + 0.000*"λαβομενη" + 0.000*"εναγισαντες"'),
(3,
'0.000*"ωργισμενους" + 0.000*"διαβοσκεσθαι" + 0.000*"απολυοντος" + 0.000*"ετοιμως" + 0.000*"σαρδονυχες" + 0.000*"νεα" + 0.000*"συμβαινει" + 0.000*"διαφωνει" + 0.000*"εξηρτημενων" + 0.000*"πενεσται"'),
(4,
'0.000*"νημερτεστατε" + 0.000*"φιλολογος" + 0.000*"απλανεσιν" + 0.000*"δηλαδη" + 0.000*"επιπλοκης" + 0.000*"θρασυλλωι" + 0.000*"διαβοσκεσθαι" + 0.000*"επωνομαζετο" + 0.000*"κρειτ" + 0.000*"φερωνυμον"'),
(5,
'0.001*"ρυπος" + 0.001*"περιτρεπομενα" + 0.000*"καταπελταισι" + 0.000*"νοτιωτερα" + 0.000*"σικελιωτας" + 0.000*"διακειμενων" + 0.000*"αποκτενουσι" + 0.000*"αποπλυσεως" + 0.000*"καθιεσαν" + 0.000*"εχομεν"'),
(6,
'0.003*"διαβοσκεσθαι" + 0.001*"δηλαδη" + 0.001*"παρακαθηται" + 0.001*"τυχης" + 0.000*"σχηματιζομενου" + 0.000*"εμφαινουσιν" + 0.000*"ανισχη" + 0.000*"πετευρον" + 0.000*"κεκληκας" + 0.000*"υποκριτη"'),
(7,
'0.000*"κριθαι" + 0.000*"συντεμνε" + 0.000*"καθαυανει" + 0.000*"κραυαλλιδαι" + 0.000*"καθισταντο" + 0.000*"εκκλητευεσθαι" + 0.000*"βαδιζουσιν" + 0.000*"πολυευκτον" + 0.000*"ταφον" + 0.000*"αρασκη"'),
(8,
'0.005*"σαμαιου" + 0.004*"λακεδαιμονα" + 0.003*"ωρμημενους" + 0.002*"εμπεδοκλεους" + 0.001*"αγομενης" + 0.001*"αναπεπεικως" + 0.001*"διηπορησθαι" + 0.001*"καθηστο" + 0.001*"βοηθησειαν" + 0.001*"βαινειν"'),
(9,
'0.001*"διαφωνει" + 0.001*"οικετων" + 0.001*"θερινη" + 0.001*"διακεκαυμενη" + 0.001*"αφοβοι" + 0.001*"απολαβουσα" + 0.001*"διεσφαλται" + 0.001*"εναλλαγαι" + 0.001*"καιροι" + 0.001*"δεομενω"')]
In [115]:
import pyLDAvis.gensim
pyLDAvis.enable_notebook()
In [116]:
pyLDAvis.gensim.prepare(lda_model, mm_corpus, id2word_tlg)
/home/kyle/cltk/venv_pypi/lib/python3.6/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing
See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
topic_term_dists = topic_term_dists.ix[topic_order]
Out[116]:
In [ ]:
Content source: kylepjohnson/ipython
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