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In [148]:



    


import pandas as pd
import spacy
import pdb
from nltk.corpus import stopwords



    











In [152]:



    


list_stopWords=list(set(stopwords.words('english')))



    











In [4]:



    


nlp = spacy.load('en_core_web_md')



    











In [5]:



    


df_demo = pd.read_csv('demo.csv',index_col=0)



    











In [6]:



    


df_demo.head()



    


















    
Out[6]:










  
    

      
      class
      date
      description
      title
    

  
  
    

      0
      Disasters and accidents
      2017/9/1
      Hurricane Irma, now a Category 2 hurricane wit...
      2017 Atlantic hurricane season
    

    

      1
      Health and medicine
      2017/9/1
      Researchers report, in the Environmental Scien...
      Great Lakes
    

    

      2
      International relations
      2017/9/1
      South Korean President Moon Jae-in and U.S. Pr...
      South Korea鈥揢nited States relations
    

    

      3
      Law and crime
      2017/9/1
      United States federal judge Richard Posner has...
      United States Court of Appeals for the Seventh...
    

    

      4
      Politics and elections
      2017/9/1
      The Labour Party, led by Jacinda Ardern, surge...
      New Zealand general election, 2017
    

  




















In [7]:



    


def class_code(type_str):
    type_str = type_str.lower()
    if 'armed' in type_str or 'attack' in type_str or 'conflict' in type_str:
        return 1
    elif 'disaster' in type_str or 'accident' in type_str:
        return 2
    elif 'law' in type_str or 'crime' in type_str:
        return 3
    elif 'politic' in type_str or 'election' in type_str:
        return 4
    elif 'international' in type_str or 'relation' in type_str:
        return 5
    elif 'science' in type_str or 'technology' in type_str:
        return 6
    elif 'business' in type_str or 'econom' in type_str:
        return 7
    elif 'art' in type_str or 'culture' in type_str:
        return 8
    elif 'sport' in type_str:
        return 9
    elif 'health' in type_str or 'environment' in type_str:
        return 10
    else:
        return 0



    











In [8]:



    


df_demo['class_code'] = df_demo['class'].apply(class_code)



    











In [9]:



    


df_demo.head()



    


















    
Out[9]:










  
    

      
      class
      date
      description
      title
      class_code
    

  
  
    

      0
      Disasters and accidents
      2017/9/1
      Hurricane Irma, now a Category 2 hurricane wit...
      2017 Atlantic hurricane season
      2
    

    

      1
      Health and medicine
      2017/9/1
      Researchers report, in the Environmental Scien...
      Great Lakes
      10
    

    

      2
      International relations
      2017/9/1
      South Korean President Moon Jae-in and U.S. Pr...
      South Korea鈥揢nited States relations
      5
    

    

      3
      Law and crime
      2017/9/1
      United States federal judge Richard Posner has...
      United States Court of Appeals for the Seventh...
      3
    

    

      4
      Politics and elections
      2017/9/1
      The Labour Party, led by Jacinda Ardern, surge...
      New Zealand general election, 2017
      4
    

  




















In [10]:



    


def description_clean(description):
    description = description.split('. (')[0]+'.'
    return description



    











In [11]:



    


df_demo['des_clean'] = df_demo['description'].apply(description_clean)
for i in df_demo.itertuples(): print(i[3]) print(i[6]) 




In [12]:



    


from spacy import displacy
from IPython.display import display, HTML



    











In [13]:



    


def class_similarity(class_text,span):
    return nlp(class_text).similarity(nlp(span))



    











In [14]:



    


def print_pos_(doc,class_text):
    doc_pos_ = []
    for token in doc:
        if token.pos_ in ['VERB']:   #['AUX','NOUN','PROPN','VERB']:
            doc_pos_.extend([token.text,token.pos_,str(class_similarity(class_text,token.text))])
    print('|'.join(doc_pos_))



    











In [15]:



    


def print_noun_chunks(doc,class_text):
    noun_chunks =[]
    for i in doc.noun_chunks:
        noun_chunks.extend([i.text,str(class_similarity(class_text,i.text))])
    print('|'.join(noun_chunks))



    











In [16]:



    


def expand_with_noun_chuncks(noun_chuncks,ent):
    for noun_chunck in noun_chuncks:
        if ent in noun_chunck:
            return noun_chunck
    return ent



    











In [32]:



    


def argument_candidate(doc,class_text):
    arguments = []
    noun_chuncks = [i.text for i in doc.noun_chunks]
    for i in doc.ents:
        #arguments.append((expand_with_noun_chuncks(noun_chuncks,i.text),i.label_,str(class_similarity(class_text,expand_with_noun_chuncks(noun_chuncks,i.text)))))
        arguments.append((i.text,i.label_))
    related_when = [i for i in arguments if i[1] in ['TIME','DATE']]
    related_where = [i for i in arguments if i[1] in ['GPE','LOC','FACILITY']]
    related_who = [i for i in arguments if i[1] in ['PERSON','NORP','ORG','']]
    related_what = [i for i in arguments if i[1] in ['PRODUCT','EVENT','WORK_OF_ART','LAW','LANGUAGE','PERCENT','MONEY','QUANTITY','ORDINAL','CARDINAL']]
    return arguments,{
        'related_when':related_when,
        'related_where':related_where,
        'related_who':related_who,
        'related_what':related_what,
    }
def trigger_candidate(doc,arguments,class_text): triggers = [] arguments = [argu[0] for argu in arguments] noun_chuncks = [i.text for i in doc.noun_chunks] triggers.extend([(i,str(class_similarity(class_text,i))) for i in set(noun_chuncks)-set(arguments)]) for token in doc: if token.tag_.startswith('V'): triggers.append((token.text,token.tag_,str(class_similarity(class_text,token.text)))) return triggers
def trigger_candidate(doc,class_text): triggers_NN = [] triggers_V = [] for token in doc: if not token.is_stop: if token.tag_.startswith('NN'): triggers_NN.append((token.text,token.tag_,str(class_similarity(class_text,token.text)))) if token.tag_.startswith('V'): triggers_V.append((token.text,token.tag_,str(class_similarity(class_text,token.text)))) triggers = sorted(triggers_NN,key=lambda x:x[2],reverse=True)[:3]+sorted(triggers_V,key=lambda x:x[2],reverse=True)[:3] return triggers 




In [84]:



    


def trigger_candidate(doc,class_text,doc_index):
    triggers = []
    for token in doc:
        #if not token.is_stop and token.tag_.startswith('NN') or token.tag_.startswith('V'):
        if not token.is_stop and token.tag_.startswith('V'):
            if token.text.lower() in EFITF[doc_index].keys():
                triggers.append((token.text,token.tag_,str(class_similarity(class_text,token.text)+EFITF[doc_index][token.text.lower()])))
    triggers = sorted(triggers,key=lambda x:x[2],reverse=True)[:3]
    return triggers



    











In [85]:



    


def print_arguments_triggers(arguments_dict,triggers):
    print('Arguments:')
    #pdb.set_trace()
    for k,v in arguments_dict.items():
        print(k+' : '+'|'+'|'.join(['|'.join(i) for i in v]))
    triggers_str = ''
    for i in triggers:
        triggers_str+='|'+'|'.join(i)
    print('Triggers:',triggers_str)



    











In [174]:



    


def get_kw_set(doc,class_text,doc_index):
    kws = []
    for i in doc.ents:
        #kws.extend(i.text.split(' '))
        kws.append(i.text)
    triggers = []
    for token in doc:
        if not token.is_stop and token.tag_.startswith('V'):
            if token.text.lower() in EFITF[doc_index].keys():
                triggers.append((token.text,token.tag_,str(class_similarity(class_text,token.text)+EFITF[doc_index][token.text.lower()])))
    triggers = sorted(triggers,key=lambda x:x[2],reverse=True)[:3]
    for i in triggers:
        kws.append(i[0])
    noun_chunks = []
    for i in doc.noun_chunks:
        noun_chunks.append((i.text,str(class_similarity(class_text,i.text))))
    #noun_chunks = sorted(noun_chunks,key=lambda x:x[1],reverse=True)[:1]
    #for i in noun_chunks:
     #   kws.extend(i[0].split(' '))
    kws.append(sorted(noun_chunks,key=lambda x:x[1],reverse=True)[0][0].split(' ')[-1])
    kws = [w for w in kws if not w in list_stopWords]
    return list(set(kws))



    











In [178]:



    


def print_kws_(kws):
    kws_str = ''
    for i in kws:
        kws_str+='|'+i
    print('kws:',str(len(kws)),kws_str)



    











In [179]:



    


def display_doc(doc,style='ent',class_text=None,doc_index=None):
    doc = nlp(doc)
    display(HTML(displacy.render(doc, style=style)))
    arguments,arguments_dict = argument_candidate(doc,class_text)
    triggers = trigger_candidate(doc,class_text,doc_index)
    print_arguments_triggers(arguments_dict,triggers)
    #print_pos_(doc,class_text)
    print_noun_chunks(doc,class_text)
    kws  = get_kw_set(doc,class_text,doc_index)
    print_kws_(kws)



    











In [180]:



    


for event in df_demo.iterrows():
    print(event[0])
    print(event[1]['class'],'\t',event[1]['date'],'\t',event[1]['title'])
    display_doc(event[1]['des_clean'],class_text=event[1]['class'].replace('and',''),doc_index=event[0])



    


















    






0
Disasters and accidents 	 2017/9/1 	 2017 Atlantic hurricane season










    









    Hurricane Irma
    PERSON

, now a 

    Category 2
    QUANTITY

 hurricane with maximum sustained winds of 

    110 miles per hour
    QUANTITY

 (

    175 kilometers per hour
    QUANTITY

), is expected to resume strengthening 

    this weekend
    DATE

 increasing the danger when it nears 

    the Leeward Islands
    LOC

 in the 

    Caribbean
    LOC

 

    next Thursday
    DATE

.










    






Arguments:
related_when : |this weekend|DATE|next Thursday|DATE
related_where : |the Leeward Islands|LOC|Caribbean|LOC
related_who : |Hurricane Irma|PERSON
related_what : |Category 2|QUANTITY|110 miles per hour|QUANTITY|175 kilometers per hour|QUANTITY
Triggers: |increasing|VBG|0.576451752579|expected|VBN|0.476465759179|strengthening|VBG|0.427861711087|resume|VB|0.36165819714|nears|VBZ|0.354343659864
Hurricane Irma|0.357908785797|now a Category 2 hurricane|0.380235448992|maximum sustained winds|0.374320373452|110 miles|0.13337395046|hour|0.162428970276|175 kilometers|0.0666239555169|hour|0.162428970276|the danger|0.462647103084|it|0.264386930191|the Leeward Islands|0.247873127015|the Caribbean|0.208435926875
kws: 12 |175 kilometers per hour|Category 2|strengthening|danger|this weekend|Caribbean|increasing|next Thursday|the Leeward Islands|Hurricane Irma|expected|110 miles per hour
1
Health and medicine 	 2017/9/1 	 Great Lakes










    







Researchers report, in 

    the Environmental Science & Technology
    ORG

 journal, the discovery of antidepressant concentrations in 

    10
    CARDINAL

 kinds of fish in 

    the Niagara River
    LOC

, which links to 

    the Great Lakes
    LOC

 via 

    Lakes Erie
    LOC

 and 

    Ontario
    GPE

. Speculated causes include inadequate treatment of water re-introduced into said bodies of water.










    






Arguments:
related_when : |
related_where : |the Niagara River|LOC|the Great Lakes|LOC|Lakes Erie|LOC|Ontario|GPE
related_who : |the Environmental Science & Technology|ORG
related_what : |10|CARDINAL
Triggers: |include|VBP|0.516214151193|report|VBP|0.511450318452|introduced|VBN|0.438623985499|said|VBN|0.42566708155|links|VBZ|0.425454435351
Researchers|0.486778847535|the Environmental Science & Technology journal|0.626985301713|the discovery|0.409114996831|antidepressant concentrations|0.621126117878|10 kinds|0.338455982602|fish|0.307684981229|the Niagara River|0.388616151142|the Great Lakes|0.358734376058|Lakes Erie|0.32894182904|Ontario|0.32870366923|Speculated causes|0.402148572707|inadequate treatment|0.606032998365|water|0.354915577487|said bodies|0.362389242075|water|0.354915577487
kws: 10 |report|the Environmental Science & Technology|include|the Niagara River|Ontario|10|the Great Lakes|Lakes Erie|introduced|journal
2
International relations 	 2017/9/1 	 South Korea鈥揢nited States relations










    









    South Korean
    NORP

 President 

    Moon Jae
    PERSON

-in and 

    U.S.
    GPE

 President 

    Donald Trump
    PERSON

 agree to revise the 

    South Korea
    GPE

 Ballistic Missile Range Guidelines which caps 

    South Korea's
    GPE

 missile development.










    






Arguments:
related_when : |
related_where : |U.S.|GPE|South Korea|GPE|South Korea's|GPE
related_who : |South Korean|NORP|Moon Jae|PERSON|Donald Trump|PERSON
related_what : |
Triggers: |revise|VB|0.53351519271|agree|VBP|0.503212040706|caps|VBZ|0.317745383302
South Korean President Moon Jae-in|0.40294889442|U.S. President Donald Trump|0.413748916657|the South Korea Ballistic Missile Range Guidelines|0.486171632835|South Korea's missile development|0.51063238924
kws: 10 |South Korea's|agree|South Korean|Donald Trump|Moon Jae|South Korea|U.S.|development|revise|caps
3
Law and crime 	 2017/9/1 	 United States Court of Appeals for the Seventh Circuit










    









    United States
    GPE

 federal judge 

    Richard Posner
    PERSON

 has announced his retirement after 

    three decades
    DATE

 of service on the bench of the 

    7th
    ORDINAL

 

    U.S. Circuit Court of Appeals
    ORG

, headquartered in 

    Chicago
    GPE

, 

    Illinois
    GPE

. Appointed by President 

    Ronald Reagan
    PERSON

 in 

    1981
    DATE

, Justice 

    Posner
    PERSON

 has written 

    more than 3,300
    CARDINAL

 opinions from the 

    bench.(AP
    ORG

).










    






Arguments:
related_when : |three decades|DATE|1981|DATE
related_where : |United States|GPE|Chicago|GPE|Illinois|GPE
related_who : |Richard Posner|PERSON|U.S. Circuit Court of Appeals|ORG|Ronald Reagan|PERSON|Posner|PERSON|bench.(AP|ORG
related_what : |7th|ORDINAL|more than 3,300|CARDINAL
Triggers: |written|VBN|0.547143896013|Appointed|VBN|0.456781502092|headquartered|VBN|0.414551141092|announced|VBN|0.369286792484
United States federal judge Richard Posner|0.631050195264|his retirement|0.423290169946|three decades|0.423120644064|service|0.284943002868|the bench|0.343527492927|the 7th U.S. Circuit Court|0.529322510022|Appeals|0.462955921122|Chicago|0.29550988158|Illinois|0.342008780656|President Ronald Reagan|0.265534860052|Justice Posner|0.45301231031|more than 3,300 opinions|0.398155457616|the bench.(AP|0.403803427413
kws: 15 |1981|three decades|Appointed|Illinois|7th|Ronald Reagan|U.S. Circuit Court of Appeals|United States|Richard Posner|Chicago|Posner|more than 3,300|written|headquartered|bench.(AP
4
Politics and elections 	 2017/9/1 	 New Zealand general election, 2017










    









    The Labour Party
    ORG

, led by 

    Jacinda Ardern
    PERSON

, surges in the polls and is neck-and-neck with 

    the National Party
    ORG

, led by 

    Bill English
    PERSON

, for the race for Prime Minister of 

    New Zealand
    GPE

.










    






Arguments:
related_when : |
related_where : |New Zealand|GPE
related_who : |The Labour Party|ORG|Jacinda Ardern|PERSON|the National Party|ORG|Bill English|PERSON
related_what : |
Triggers: |led|VBN|0.697825518784|led|VBN|0.697825518784|surges|VBZ|0.473210110132
The Labour Party|0.574051702776|Jacinda Ardern|0.021859615811|the polls|0.690135010383|neck|0.0705136826232|-neck|0.0|the National Party|0.589717172463|Bill English|0.352255377966|the race|0.450207550477|Prime Minister|0.48925934282|New Zealand|0.254721162151
kws: 8 |polls|The Labour Party|New Zealand|Jacinda Ardern|surges|led|Bill English|the National Party
5
Politics and elections 	 2017/9/1 	 Kenyan general election, 2017










    









    The Supreme Court
    ORG

 of 

    Kenya
    GPE

 annuls the results of the recent presidential election, that indicated President 

    Uhuru Kenyatta
    PERSON

 was reelected, due to irregularities, and orders a new election.










    






Arguments:
related_when : |
related_where : |Kenya|GPE
related_who : |The Supreme Court|ORG|Uhuru Kenyatta|PERSON
related_what : |
Triggers: |reelected|VBN|0.903135476102|indicated|VBD|0.486114605134|annuls|VBP|0.443648421403
The Supreme Court|0.446410985937|Kenya|0.257459323197|the results|0.359382037781|the recent presidential election|0.793275848827|President Uhuru Kenyatta|0.274939847061|irregularities|0.186572255|a new election|0.653051979632
kws: 7 |reelected|Uhuru Kenyatta|election|Kenya|annuls|The Supreme Court|indicated
6
Politics and elections 	 2017/9/1 	 Presidency of Donald Trump










    









    Paul Ryan
    PERSON

 (R-WI), 

    U.S.
    GPE

 Speaker of 

    the House of Representatives
    ORG

, urges President 

    Donald Trump
    PERSON

 not to rescind 

    the Deferred Action for Childhood Arrivals
    ORG

 (

    DACA
    ORG

) program that protects immigrants who illegally entered the 

    U.S.
    GPE

 as children from deportation.










    






Arguments:
related_when : |
related_where : |U.S.|GPE|U.S.|GPE
related_who : |Paul Ryan|PERSON|the House of Representatives|ORG|Donald Trump|PERSON|the Deferred Action for Childhood Arrivals|ORG|DACA|ORG
related_what : |
Triggers: |urges|VBZ|0.561677213396|rescind|VB|0.52508074443|entered|VBD|0.444119911808|protects|VBZ|0.295272475197
Paul Ryan|0.250817680569|R-WI|0.147229129185|U.S. Speaker|0.369958426959|the House|0.325866711781|Representatives|0.431843478778|President Donald Trump|0.482175436017|the Deferred Action|0.393154236352|Childhood Arrivals (DACA) program|0.242730180572|immigrants|0.396761080045|who|0.298166567757|the U.S.|0.437061635613|children|0.219449375835|deportation|0.290521074462
kws: 10 |entered|urges|Donald Trump|the House of Representatives|DACA|rescind|Trump|the Deferred Action for Childhood Arrivals|U.S.|Paul Ryan
7
Science and technology 	 2017/9/1 	 2017 in astronomy










    









    Asteroid 3122 Florence
    PERSON

, which is 

    roughly 2.7 miles
    QUANTITY

 (

    4.4 kilometers
    QUANTITY

) wide, comes within 

    4.4 million miles
    QUANTITY

 (

    7 million km
    QUANTITY

) of 

    Earth
    LOC

 鈥?approximately 

    18
    CARDINAL

 times the distance from our planet to the 

    Moon
    PERSON

.










    






Arguments:
related_when : |
related_where : |Earth|LOC
related_who : |Asteroid 3122 Florence|PERSON|Moon|PERSON
related_what : |roughly 2.7 miles|QUANTITY|4.4 kilometers|QUANTITY|4.4 million miles|QUANTITY|7 million km|QUANTITY|18|CARDINAL
Triggers: |comes|VBZ|0.622712346803
Asteroid|0.20533818023|3122 Florence|0.08808609462|4.4 kilometers|-0.00234738796058|4.4 million miles|0.143568291562|7 million km|0.119574371354|Earth|0.415404542524|our planet|0.471326009147|the Moon|0.362654722181
kws: 10 |comes|7 million km|roughly 2.7 miles|Asteroid 3122 Florence|planet|18|4.4 million miles|Moon|4.4 kilometers|Earth
8
Armed attacks and conflicts 	 2017/9/2 	 Iraqi Civil War










    









    Seven
    CARDINAL

 people are killed and 

    13
    CARDINAL

 are injured after suicide bombers hit a state-run power station near the northern city of 

    Samarra
    GPE

, 

    Iraq
    GPE

. 

    The Islamic State of
    ORG

 

    Iraq
    GPE

 and the 

    Levant
    ORG

 claims responsibility for the attack.










    






Arguments:
related_when : |
related_where : |Samarra|GPE|Iraq|GPE|Iraq|GPE
related_who : |The Islamic State of|ORG|Levant|ORG
related_what : |Seven|CARDINAL|13|CARDINAL
Triggers: |killed|VBN|0.756264779291|injured|VBN|0.635162439802|claims|VBZ|0.630417833733|hit|VBD|0.516748470061|run|VBN|0.501444536372
Seven people|0.499983535135|suicide bombers|0.620420973623|a state-run power station|0.42076337093|the northern city|0.429933340975|Samarra|0.0|Iraq|0.371648268077|The Islamic State|0.480012098147|Iraq|0.371648268077|the Levant|0.0788780730697|responsibility|0.483108068312|the attack|0.703502175466
kws: 10 |Samarra|killed|Levant|injured|claims|attack|The Islamic State of|Iraq|13|Seven
9
Business and economy 	 2017/9/2 	 Trump Tower wiretapping allegations










    







Both the 

    FBI
    ORG

 and 

    NSD
    ORG

 declare that they possess no records indicating that 

    Trump Tower
    ORG

 in 

    New York City
    GPE

, 

    New York
    GPE

 was wiretapped 

    earlier in March
    DATE

.










    






Arguments:
related_when : |earlier in March|DATE
related_where : |New York City|GPE|New York|GPE
related_who : |FBI|ORG|NSD|ORG|Trump Tower|ORG
related_what : |
Triggers: |possess|VBP|0.515733289038|indicating|VBG|0.498797109917|wiretapped|VBN|0.481607520016|declare|VB|0.47655761179
Both the FBI|0.414553402528|NSD|-0.110817321485|they|0.433423319239|no records|0.340568678527|Trump Tower|0.292076124271|New York City|0.455834564546|March|0.189796103938
kws: 10 |possess|earlier in March|City|New York City|wiretapped|indicating|NSD|New York|Trump Tower|FBI
10
Disasters and accidents 	 2017/9/2 	 2017 disasters in Kenya










    









    Seven
    CARDINAL

 schoolgirls are killed and 

    ten
    CARDINAL

 hospitalised after a fire at 

    the Moi Girls School
    ORG

 in 

    Nairobi
    GPE

, 

    Kenya
    GPE

.










    






Arguments:
related_when : |
related_where : |Nairobi|GPE|Kenya|GPE
related_who : |the Moi Girls School|ORG
related_what : |Seven|CARDINAL|ten|CARDINAL
Triggers: |hospitalised|VBN|0.774052316353|killed|VBN|0.676026278259
Seven schoolgirls|0.218349128256|a fire|0.330839454957|the Moi Girls School|0.22810133241|Nairobi|0.222126340306|Kenya|0.222126340306
kws: 8 |ten|fire|Kenya|the Moi Girls School|killed|Nairobi|hospitalised|Seven
11
International relations 	 2017/9/2 	 Timor Gap










    









    Australia
    GPE

 and 

    East Timor
    GPE

 settle a dispute between the 

    two
    CARDINAL

 countries in 

    the Timor Sea
    LOC

.










    






Arguments:
related_when : |
related_where : |Australia|GPE|East Timor|GPE|the Timor Sea|LOC
related_who : |
related_what : |two|CARDINAL
Triggers: |settle|VBP|0.642970629479
Australia|0.313285475626|East Timor|0.278371843071|a dispute|0.509121296394|the two countries|0.584559746707|the Timor Sea|0.365749520623
kws: 6 |two|countries|Australia|East Timor|the Timor Sea|settle
12
Law and crime 	 2017/9/2 	 Law enforcement in Cambodia










    









    Cambodian
    NORP

 opposition leader 

    Kem Sokha
    PERSON

 is arrested for alleged treason.










    






Arguments:
related_when : |
related_where : |
related_who : |Cambodian|NORP|Kem Sokha|PERSON
related_what : |
Triggers: |alleged|VBN|0.891774744782|arrested|VBN|0.834817634431
Cambodian opposition leader Kem Sokha|0.342590643695|alleged treason|0.581041341985
kws: 5 |treason|Kem Sokha|Cambodian|arrested|alleged
13
Business and economy 	 2017/9/3 	 Economy of Cambodia










    









    The Cambodia Daily newspaper
    ORG

 announces it will publish its final edition after being ordered to pay 

    USD$6.3 million
    CARDINAL

 in taxes as a result of an investigation, initiated by Prime Minister of 

    Cambodia
    GPE

 

    Hun Sen
    PERSON

, into private companies operating in 

    Cambodia
    GPE

.










    






Arguments:
related_when : |
related_where : |Cambodia|GPE|Cambodia|GPE
related_who : |The Cambodia Daily newspaper|ORG|Hun Sen|PERSON
related_what : |USD$6.3 million|CARDINAL
Triggers: |operating|VBG|0.662886759688|pay|VB|0.65640424901|initiated|VBN|0.444462779822|publish|VB|0.427345580133|announces|VBZ|0.42408346855
The Cambodia Daily newspaper|0.470491825123|it|0.471583147914|its final edition|0.367476797751|taxes|0.545024875318|a result|0.443271208147|an investigation|0.380493395073|Prime Minister|0.382542511416|Cambodia Hun Sen|0.0984757782364|private companies|0.624084544149|Cambodia|0.222366604159
kws: 8 |The Cambodia Daily newspaper|Hun Sen|operating|USD$6.3 million|pay|companies|initiated|Cambodia
14
Disasters and accidents 	 2017/9/3 	 Hurricane Harvey










    









    Hurricane Harvey
    PERSON

 is projected to possibly become the 

    second
    ORDINAL

 costliest hurricane in 

    the United States's
    GPE

 history, with estimates ranging from 

    US$72 billion
    MONEY

 to 

    over $125 billion
    MONEY

. In comparison, 

    Hurricane Katrina's
    EVENT

 total damage is estimated to be 

    around $118 to $160 billion
    MONEY

.










    






Arguments:
related_when : |
related_where : |the United States's|GPE
related_who : |Hurricane Harvey|PERSON
related_what : |second|ORDINAL|US$72 billion|MONEY|over $125 billion|MONEY|Hurricane Katrina's|EVENT|around $118 to $160 billion|MONEY
Triggers: |estimated|VBN|0.530604267815|ranging|VBG|0.502124779847|projected|VBN|0.477080701963
Hurricane Harvey|0.392259451014|the second costliest hurricane|0.35900132965|the United States's history|0.351613673761|estimates|0.318047078796|comparison|0.175073361951|Hurricane Katrina's total damage|0.565762162693
kws: 11 |damage|over $125 billion|US$72 billion|around $118 to $160 billion|projected|ranging|Hurricane Katrina's|the United States's|second|Hurricane Harvey|estimated
15
Disasters and accidents 	 2017/9/3 	 2017 California wildfires










    









    The La Tuna Fire
    ORG

 continues to burn in 

    Los Angeles
    GPE

, 

    California
    GPE

, becoming the largest fire in the city's history at 

    around 5,800 acres
    QUANTITY

. Mayor 

    Eric Garcetti
    PERSON

 declares a state of emergency.










    






Arguments:
related_when : |
related_where : |Los Angeles|GPE|California|GPE
related_who : |The La Tuna Fire|ORG|Eric Garcetti|PERSON
related_what : |around 5,800 acres|QUANTITY
Triggers: |continues|VBZ|0.525916660585|burn|VB|0.511758263486|declares|VBZ|0.450082252701
The La Tuna Fire|0.288991587071|Los Angeles|0.203899121292|California|0.266263241727|the largest fire|0.366123665129|the city's history|0.315794170104|around 5,800 acres|0.234410498845|Mayor Eric Garcetti|0.146733920305|a state|0.260954882189|emergency|0.507407083596
kws: 9 |The La Tuna Fire|continues|California|burn|emergency|Eric Garcetti|declares|around 5,800 acres|Los Angeles
16
Disasters and accidents 	 2017/9/3 	 Unexploded ordnance










    







Large portions of 

    Frankfurt
    GPE

, 

    Germany
    GPE

, are evacuated as local authorities work to defuse a bomb left over from a 

    Royal Air Force
    ORG

 raid during 

    World War II
    EVENT

. The evacuation is the largest to occur in 

    Europe
    LOC

 since 

    World War II
    EVENT

.










    






Arguments:
related_when : |
related_where : |Frankfurt|GPE|Germany|GPE|Europe|LOC
related_who : |Royal Air Force|ORG
related_what : |World War II|EVENT|World War II|EVENT
Triggers: |occur|VB|0.736911283715|defuse|VB|0.578842760554|work|VBP|0.453727826499|evacuated|VBN|0.439276559012|left|VBN|0.333185006519
Large portions|0.22377748531|Frankfurt|0.108952229158|Germany|0.169187339897|local authorities|0.351156004567|a bomb|0.27832351347|a Royal Air Force raid|0.270302638226|World War II|0.361291829215|The evacuation|0.474433452575|Europe|0.217309333547|World War II|0.361291829215
kws: 9 |work|World War II|defuse|Europe|evacuation|occur|Germany|Frankfurt|Royal Air Force
17
International relations 	 2017/9/3 	 2017 North Korea crisis










    









    2017
    CARDINAL

 

    North Korean
    NORP

 nuclear test

    


    FAC

A 

    6.3
    CARDINAL

 magnitude earthquake is detected near 

    the Punggye-ri Nuclear Test Site
    LOC

 in 

    Kilju County
    GPE

, 

    North Korea
    GPE

, after 

    North Korea
    GPE

 tests its 

    sixth
    ORDINAL

 and most powerful nuclear weapon to date. 

    North Korean
    NORP

 state media claims the country tested a hydrogen bomb that can be fitted on an 

    ICBM
    NORP

.










    






Arguments:
related_when : |
related_where : |the Punggye-ri Nuclear Test Site|LOC|Kilju County|GPE|North Korea|GPE|North Korea|GPE
related_who : |North Korean|NORP|North Korean|NORP|ICBM|NORP
related_what : |2017|CARDINAL|6.3|CARDINAL|sixth|ORDINAL
Triggers: |claims|VBZ|0.496924830016|tests|VBZ|0.380416228299|tested|VBD|0.308671368003|detected|VBN|0.236958337819|fitted|VBN|0.202453237052
2017 North Korean nuclear test|0.427116084126|A 6.3 magnitude earthquake|0.266066753153|the Punggye-ri Nuclear Test Site|0.436814241617|Kilju County|0.215681359642|North Korea|0.343575725129|North Korea|0.343575725129|its sixth and most powerful nuclear weapon|0.484554837886|date|0.206718475618|North Korean state media|0.528687920285|the country|0.538792147084|a hydrogen bomb|0.253070026941|an ICBM|0.319470720851
kws: 13 |country|North Korea|

|ICBM|tested|tests|the Punggye-ri Nuclear Test Site|claims|6.3|Kilju County|North Korean|2017|sixth
18
Science and technology 	 2017/9/3 	 Free-electron laser










    







The 

    European
    NORP

 X-ray free-electron laser is inaugurated in 

    Hamburg
    GPE

, 

    Germany
    GPE

.










    






Arguments:
related_when : |
related_where : |Hamburg|GPE|Germany|GPE
related_who : |European|NORP
related_what : |
Triggers: |inaugurated|VBN|0.578445420642
The European X-ray free-electron laser|0.424491526623|Hamburg|0.16417236916|Germany|0.22523847438
kws: 5 |European|Hamburg|Germany|inaugurated|laser
19
Armed attacks and conflicts 	 2017/9/4 	 Syrian Civil War










    









    Syrian
    NORP

 state television reports 

    the Syrian Army
    ORG

 reaches a point 

    3 kilometers
    QUANTITY

 from 

    Deir
    GPE

 ez-Zor, a city besieged by the 

    Islamic
    NORP

 State of 

    Iraq
    GPE

 and the 

    Levant
    ORG

 since 

    2014
    DATE

.










    






Arguments:
related_when : |2014|DATE
related_where : |Deir|GPE|Iraq|GPE
related_who : |Syrian|NORP|the Syrian Army|ORG|Islamic|NORP|Levant|ORG
related_what : |3 kilometers|QUANTITY
Triggers: |besieged|VBN|0.772017395008|reports|VBZ|0.636829825263|reaches|VBZ|0.464214869605
Syrian state television|0.438559804509|the Syrian Army|0.573829628291|a point|0.370069577379|Deir ez-Zor|-0.0590066802067|a city|0.363040196838|the Islamic State|0.480012098147|Iraq|0.371648268077|the Levant|0.0788780730697
kws: 12 |3 kilometers|2014|besieged|Levant|the Syrian Army|reports|Deir|reaches|Iraq|Syrian|Army|Islamic
20
Business and economy 	 2017/9/4 	 Economy of the United States










    









    United Technologies Corp
    ORG

 will buy airplane parts maker 

    Rockwell Collins
    ORG

 for 

    USD$30 Billion
    FAC

, including 

    seven billion
    CARDINAL

 in debt previously incurred by 

    Rockwell Collins
    PERSON

.










    






Arguments:
related_when : |
related_where : |
related_who : |United Technologies Corp|ORG|Rockwell Collins|ORG|Rockwell Collins|PERSON
related_what : |seven billion|CARDINAL
Triggers: |incurred|VBN|0.680928468114|including|VBG|0.553071667154|buy|VB|0.498794526875
United Technologies Corp|0.506393096195|airplane parts maker|0.383601014482|Rockwell Collins|0.105153675115|USD$30 Billion|0.431948473239|debt|0.546465344851|Rockwell Collins|0.105153675115
kws: 8 |including|incurred|seven billion|USD$30 Billion|United Technologies Corp|Rockwell Collins|buy|debt
21
Business and economy 	 2017/9/4 	 Media of the United States










    









    Tronc Inc.
    ORG

, 

    the Los Angeles Times
    ORG

 and 

    the Chicago Tribune
    ORG

 publisher, buys 

    the New York Daily News
    ORG

.










    






Arguments:
related_when : |
related_where : |
related_who : |Tronc Inc.|ORG|the Los Angeles Times|ORG|the Chicago Tribune|ORG|the New York Daily News|ORG
related_what : |
Triggers: |buys|VBZ|0.604804845472
Tronc Inc.|0.186215667038|the Los Angeles Times|0.333799038299|the Chicago Tribune publisher|0.434655137008|the New York Daily News|0.516343650488
kws: 6 |the Los Angeles Times|buys|the Chicago Tribune|News|the New York Daily News|Tronc Inc.
22
Disasters and accidents 	 2017/9/4 	 2017 Atlantic hurricane season










    









    Hurricane Irma
    EVENT




    Hurricane Irma
    PERSON

 is now a 

    Category 4
    QUANTITY

 hurricane with maximum sustained winds of 

    130 mph
    QUANTITY

 (

    215 km/h
    QUANTITY

). 

    Hurricane
    EVENT

 warnings are issued for 

    the Leeward Islands
    LOC

 in the 

    Caribbean
    LOC

, which are expected to be affected 

    Tuesday
    DATE

. 

    Rainfalls
    PERSON

 of 

    up to 10 inches
    QUANTITY

 (

    25 centimeters
    QUANTITY

) are possible. 

    Irma
    PERSON

 is forecast to strengthen over 

    the next 48 hours
    TIME

. The governors of 

    Puerto Rico
    GPE

 and 

    Florida
    GPE

 issue states of emergency.










    






Arguments:
related_when : |Tuesday|DATE|the next 48 hours|TIME
related_where : |the Leeward Islands|LOC|Caribbean|LOC|Puerto Rico|GPE|Florida|GPE
related_who : |Hurricane Irma|PERSON|Rainfalls|PERSON|Irma|PERSON
related_what : |Hurricane Irma|EVENT|Category 4|QUANTITY|130 mph|QUANTITY|215 km/h|QUANTITY|Hurricane|EVENT|up to 10 inches|QUANTITY|25 centimeters|QUANTITY
Triggers: |affected|VBN|0.697770825858|forecast|VBN|0.560290394748|expected|VBN|0.436268096129|strengthen|VB|0.361954692989|issued|VBN|0.345393822104
Hurricane Irma|0.357908785797|Hurricane Irma|0.357908785797|a Category 4 hurricane|0.375361751773|maximum sustained winds|0.374320373452|130 mph|0.179235986107|215 km|0.0646489319549|h|0.0384976888206|Hurricane warnings|0.561845313666|the Leeward Islands|0.247873127015|the Caribbean|0.208435926875|Rainfalls|0.0|up to 10 inches|0.162286805315|25 centimeters|0.0562783222763|Irma|-0.00522683117666|the next 48 hours|0.203661716508|The governors|0.203248037528|Puerto Rico|0.136524945534|Florida|0.247440250985|states|0.327626734376|emergency|0.507407083596
kws: 19 |forecast|affected|25 centimeters|Caribbean|Tuesday|Rainfalls|the next 48 hours|Puerto Rico|Florida|warnings|215 km/h|up to 10 inches|the Leeward Islands|Hurricane Irma|Category 4|expected|Irma|130 mph|Hurricane
23
International relations 	 2017/9/4 	 2017 North Korea crisis










    









    South Korean
    NORP

 Defense Minister 

    Song Young
    PERSON

-moo says it is worth reviewing deployment of 

    U.S.
    GPE

 strategic assets (aircraft carriers, nuclear submarines, and 

    B-52
    PRODUCT

 bombers) to 

    South Korea
    GPE

 more regularly.










    






Arguments:
related_when : |
related_where : |U.S.|GPE|South Korea|GPE
related_who : |South Korean|NORP|Song Young|PERSON
related_what : |B-52|PRODUCT
Triggers: |says|VBZ|0.492164525813|reviewing|VBG|0.483029829469
South Korean Defense Minister Song Young-moo|0.408137876699|it|0.310377940963|deployment|0.254030296323|U.S. strategic assets|0.584852217979|aircraft carriers|0.312171874531|nuclear submarines|0.334960129915|B-52 bombers|0.145975781535|South Korea|0.351919548799
kws: 8 |B-52|says|South Korean|assets|Song Young|South Korea|U.S.|reviewing
24
International relations 	 2017/9/4 	 Crisis in Venezuela










    







The opposition movement in 

    Venezuela
    GPE

 seeks help from 

    France
    GPE

.










    






Arguments:
related_when : |
related_where : |Venezuela|GPE|France|GPE
related_who : |
related_what : |
Triggers: |seeks|VBZ|0.815549345108
The opposition movement|0.482940938463|Venezuela|0.196721537552|help|0.342547838316|France|0.315508702161
kws: 4 |France|Venezuela|seeks|movement
25
Law and crime 	 2017/9/4 	 Crime in Italy










    









    Italian
    NORP

 fugitive and 'Ndrangheta member 

    Rocco Morabito
    PERSON

 is arrested in 

    Montevideo
    GPE

, 

    Uruguay
    GPE

, after 

    23 years
    DATE

 on the run. He is now expected to be extradited to 

    Italy
    GPE

 in 

    the coming months
    DATE

.










    






Arguments:
related_when : |23 years|DATE|the coming months|DATE
related_where : |Montevideo|GPE|Uruguay|GPE|Italy|GPE
related_who : |Italian|NORP|Rocco Morabito|PERSON
related_what : |
Triggers: |arrested|VBN|0.733222805235|extradited|VBN|0.713772662533|coming|VBG|0.583167302493|expected|VBN|0.518465246371
Italian fugitive and 'Ndrangheta member Rocco Morabito|0.559902132756|Montevideo|0.12830496218|Uruguay|0.12830496218|23 years|0.295314025423|the run|0.394003976412|He|0.446581606332|Italy|0.17969688972|the coming months|0.385382241268
kws: 11 |Rocco Morabito|Italy|Italian|Montevideo|extradited|the coming months|arrested|Morabito|Uruguay|23 years|coming
26
Politics and elections 	 2017/9/4 	 Politics of Taiwan










    







Premier of 

    the Republic of China
    GPE

 

    Lin Chuan
    PERSON

 offers his resignation as head of 

    the Executive Branch
    ORG

 of 

    Taiwan
    GPE

.










    






Arguments:
related_when : |
related_where : |the Republic of China|GPE|Taiwan|GPE
related_who : |Lin Chuan|PERSON|the Executive Branch|ORG
related_what : |
Triggers: |offers|VBZ|0.359036736913
Premier|0.199533175525|the Republic|0.534595955881|China|0.156207825947|Lin Chuan|0.0236051202478|his resignation|0.443677278273|head|0.20214664283|the Executive Branch|0.39457847627|Taiwan|0.15698466789
kws: 6 |Lin Chuan|offers|Taiwan|the Republic of China|Republic|the Executive Branch
27
Armed conflicts and attacks 	 2017/9/5 	 Syrian Civil War










    







Siege of Deir ez-Zor (

    2014鈥?7
    PERSON

)

    


    GPE

The 

    Syrian Army
    ORG

 lifts the 

    28-month
    CARDINAL

-long siege of 

    Deir
    PRODUCT

 ez-Zor by 

    the Islamic State of Iraq
    ORG

 and the 

    Levant
    ORG

.










    






Arguments:
related_when : |
related_where : |

|GPE
related_who : |2014鈥?7|PERSON|Syrian Army|ORG|the Islamic State of Iraq|ORG|Levant|ORG
related_what : |28-month|CARDINAL|Deir|PRODUCT
Triggers: |lifts|VBZ|0.330900978673
Siege|0.521823476154|Deir ez-Zor|-0.0590066792325|2014鈥?7|0.0|The Syrian Army|0.573829618817|the 28-month-long siege|0.470934522013|Deir ez-Zor|-0.0590066792325|the Islamic State|0.480012090222|Iraq|0.371648261941|the Levant|0.0788780717674
kws: 9 |

|Syrian Army|the Islamic State of Iraq|28-month|Levant|2014鈥?7|lifts|Deir|Army
28
Armed conflicts and attacks 	 2017/9/5 	 2016鈥?7 Rohingya persecution in Myanmar










    









    More than 123,000
    CARDINAL

 

    Rohingya
    PERSON

 refugees have fled 

    Myanmar
    GPE

 and crossed into 

    Bangladesh
    GPE

 due to escalating violence by 

    the Myanmar Army
    ORG

.










    






Arguments:
related_when : |
related_where : |Myanmar|GPE|Bangladesh|GPE
related_who : |Rohingya|PERSON|the Myanmar Army|ORG
related_what : |More than 123,000|CARDINAL
Triggers: |escalating|VBG|0.824328611006|fled|VBN|0.740865661052|crossed|VBD|0.615631019066
More than 123,000 Rohingya refugees|0.476712677498|Myanmar|0.110897563343|Bangladesh|0.243876441692|escalating violence|0.70722532895|the Myanmar Army|0.516522867508
kws: 9 |the Myanmar Army|escalating|crossed|Myanmar|Bangladesh|Rohingya|fled|violence|More than 123,000
29
Disasters and accidents 	 2017/9/5 	 2017 Atlantic hurricane season










    









    Hurricane Irma
    EVENT




    Hurricane Irma
    EVENT

 strengthens to a maximum 

    Category 5
    QUANTITY

 hurricane, becoming the strongest 

    Atlantic
    LOC

 hurricane since 

    2005
    DATE

's 

    Hurricane Wilma
    EVENT

 in terms of maximum sustained winds, described as "extremely dangerous." 

    The National Hurricane Center
    WORK_OF_ART

 (

    NHC
    ORG

) predicts that 

    Irma
    PERSON

 could strengthen even more due to favorable conditions.










    






Arguments:
related_when : |2005|DATE
related_where : |Atlantic|LOC
related_who : |NHC|ORG|Irma|PERSON
related_what : |Hurricane Irma|EVENT|Hurricane Irma|EVENT|Category 5|QUANTITY|Hurricane Wilma|EVENT|The National Hurricane Center|WORK_OF_ART
Triggers: |predicts|VBZ|0.531750704924|strengthens|VBZ|0.404163109524|described|VBN|0.392599268983|strengthen|VB|0.384446616556
Hurricane Irma|0.357908785797|Hurricane Irma|0.357908785797|a maximum Category 5 hurricane|0.360076674442|the strongest Atlantic hurricane|0.420068582962|2005's Hurricane Wilma|0.345699893042|terms|0.242992989322|maximum sustained winds|0.374320373452|The National Hurricane Center|0.418866405429|(NHC|-0.00701237427904|Irma|-0.00522683117666|favorable conditions|0.380056787407
kws: 12 |strengthens|NHC|predicts|Category 5|Hurricane Wilma|hurricane|described|Atlantic|Hurricane Irma|The National Hurricane Center|Irma|2005
30
International relations 	 2017/9/5 	 India鈥揗yanmar relations










    









    Indian
    NORP

 Prime Minister 

    Narendra Modi
    PERSON

 heads to 

    Myanmar
    GPE

 for a state visit.










    






Arguments:
related_when : |
related_where : |Myanmar|GPE
related_who : |Indian|NORP|Narendra Modi|PERSON
related_what : |
Triggers: 
Indian Prime Minister Narendra Modi|0.333002686707|Myanmar|0.268959022181|a state visit|0.433511615993
kws: 4 |Myanmar|visit|Narendra Modi|Indian
31
International relations 	 2017/9/5 	 2017 North Korea crisis










    









    Japan鈥揢nited States
    GPE

 relations, 

    South Korea鈥揢nited States
    GPE

 relations

    


    FAC

Amid tensions from 

    South Korea
    GPE

, 

    U.S.
    GPE

 President 

    Donald Trump
    PERSON

 announces that he is "allowing 

    Japan
    GPE

 and 

    South Korea
    GPE

 to buy a substantially increased amount of highly sophisticated military equipment from 

    the United States
    GPE

." (

    The Independent
    ORG

).










    






Arguments:
related_when : |
related_where : |Japan鈥揢nited States|GPE|South Korea鈥揢nited States|GPE|South Korea|GPE|U.S.|GPE|Japan|GPE|South Korea|GPE|the United States|GPE
related_who : |Donald Trump|PERSON|The Independent|ORG
related_what : |
Triggers: |increased|VBN|0.523349314278|allowing|VBG|0.514175296079|announces|VBZ|0.450125527366|buy|VB|0.349803020058
Japan鈥揢nited States relations|0.793477104429|South Korea鈥揢nited States relations|0.708819330836|tensions|0.306615237557|South Korea|0.351919548799|U.S. President Donald Trump|0.413748916657|he|0.329023423376|Japan|0.286642157745|South Korea|0.351919548799|a substantially increased amount|0.356462363157|highly sophisticated military equipment|0.500948522712|the United States|0.554561069776|(The Independent|0.442440109051
kws: 13 |South Korea鈥揢nited States|

|announces|Japan鈥揢nited States|Japan|Donald Trump|relations|allowing|increased|The Independent|South Korea|U.S.|the United States
32
Law and crime 	 2017/9/5 	 Journalists killed in India










    







Prominent 

    Indian
    NORP

 journalist and 

    Lankesh Patrike
    PERSON

 editor 

    Gauri Lankesh
    PERSON

 is shot dead by unidentified men outside her house in the city of 

    Bengaluru
    GPE

.










    






Arguments:
related_when : |
related_where : |Bengaluru|GPE
related_who : |Indian|NORP|Lankesh Patrike|PERSON|Gauri Lankesh|PERSON
related_what : |
Triggers: |shot|VBN|0.515839042646
Prominent Indian journalist|0.42397370554|Lankesh Patrike editor|0.17564787189|Gauri Lankesh|0.0|unidentified men|0.388254146727|her house|0.409643719279|the city|0.500354653334|Bengaluru|0.0651254167868
kws: 6 |Gauri Lankesh|city|Indian|Bengaluru|shot|Lankesh Patrike
33
Law and crime 	 2017/9/5 	 Australian Marriage Law Postal Survey (Same-sex marriage)










    









    The High Court of Australia
    ORG

 receives a complaint against the 

    Australian
    NORP

 Marriage Law Postal Survey citing the postal survey as "unique and offensive." 

    (The Guardian)
    WORK_OF_ART

.










    






Arguments:
related_when : |
related_where : |
related_who : |The High Court of Australia|ORG|Australian|NORP
related_what : |(The Guardian)|WORK_OF_ART
Triggers: |citing|VBG|0.572195904063|receives|VBZ|0.425439919036
The High Court|0.593857575155|Australia|0.25587200476|a complaint|0.515442505477|the Australian Marriage Law Postal Survey|0.711241499187|the postal survey|0.453356289133|(The Guardian|0.413507017033
kws: 6 |The High Court of Australia|citing|Survey|Australian|receives|(The Guardian)
34
Politics and elections 	 2017/9/5 	 Politics of Taiwan










    









    Tainan
    GPE

 Mayor 

    William Lai Ching-te
    PERSON

 is appointed as the new Premier by the President of 

    the Republic of China
    GPE

 

    Tsai Ing-wen
    PERSON

.










    






Arguments:
related_when : |
related_where : |Tainan|GPE|the Republic of China|GPE
related_who : |William Lai Ching-te|PERSON|Tsai Ing-wen|PERSON
related_what : |
Triggers: |appointed|VBN|0.551664357862
Tainan Mayor William Lai Ching-te|0.269547018921|the new Premier|0.332105353284|the President|0.521588055935|the Republic|0.534595955881|China|0.156207825947|Tsai Ing-wen|0.1266557292
kws: 6 |Tsai Ing-wen|appointed|the Republic of China|Republic|Tainan|William Lai Ching-te
35
Politics and elections 	 2017/9/5 	 Deferred Action for Childhood Arrivals










    









    The Trump Administration
    ORG

 announces that, during 

    the next six months
    DATE

, it will be ending the 

    DACA
    ORG

 program that has halted the deportation of 

    about 800,000
    CARDINAL

 people who were brought to the 

    U.S.
    GPE

 illegally as children. 

    Congress
    ORG

 is called upon to pass legislation to correct the situation.










    






Arguments:
related_when : |the next six months|DATE
related_where : |U.S.|GPE
related_who : |The Trump Administration|ORG|DACA|ORG|Congress|ORG
related_what : |about 800,000|CARDINAL
Triggers: |brought|VBN|0.551158832215|ending|VBG|0.533632073921|halted|VBN|0.500590097025|correct|VB|0.484616554518|called|VBN|0.478593183908
The Trump Administration|0.591466005224|the next six months|0.338907360369|it|0.359504523576|the DACA program|0.280110416205|the deportation|0.395922391098|about 800,000 people|0.327572046668|who|0.298166567757|the U.S.|0.437061635613|children|0.219449375835|Congress|0.57855267145|legislation|0.497035700057|the situation|0.45906628286
kws: 10 |The Trump Administration|halted|Congress|DACA|brought|ending|about 800,000|the next six months|Administration|U.S.
36
Sports 	 2017/9/5 	 2017鈥?8 NBA season










    







Billionaire investor 

    Tilman Fertitta
    PERSON

 buys the 

    Houston Rockets NBA
    ORG

 basketball team for 

    US$2.2 billion
    MONEY

.










    






Arguments:
related_when : |
related_where : |
related_who : |Tilman Fertitta|PERSON|Houston Rockets NBA|ORG
related_what : |US$2.2 billion|MONEY
Triggers: |buys|VBZ|0.472414864905
Billionaire investor Tilman Fertitta|0.11165074302|the Houston Rockets NBA basketball team|0.599674816765
kws: 5 |buys|team|US$2.2 billion|Houston Rockets NBA|Tilman Fertitta

















In [181]:



    


from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from collections import defaultdict



    











In [97]:



    


def efitf(X):
    count = CountVectorizer(stop_words='english')
    X_train_count = count.fit_transform(X)
    tfidf = TfidfTransformer(use_idf=True,smooth_idf=True,sublinear_tf=True)
    X_train_tfidf = tfidf.fit_transform(X_train_count)
    tf_feature_names = count.get_feature_names()
    X_train_tfidf = [list(i) for i in list(X_train_tfidf.toarray())]
    EFITF = defaultdict(dict)
    for Type,values in enumerate(X_train_tfidf):
        for index,value in enumerate(values):
            if value > 0.0:
                EFITF[Type].update({tf_feature_names[index]:value}) 
    return EFITF



    











In [98]:



    


X = []



    











In [99]:



    


X = df_demo['des_clean'].tolist()



    











In [100]:



    


EFITF = efitf(X)
EFITF[0] 




In [104]:



    


'demo'



    


















    
Out[104]:








['demo']

















In [144]:



    


a = [1,2,3,4,5]



    











In [146]:



    


a



    


















    
Out[146]:








[1, 3, 4, 5]

















In [167]:



    


doc = nlp('arrested')



    











In [168]:



    


doc[0].pos_,doc[0].tag_



    


















    
Out[168]:








('VERB', 'VBN')

















In [ ]:

Content source: phiedulxp/tweet_search

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