spaCy (https://spacy.io/) is an open-source Python library that parses and "understands" large volumes of text. Separate models are available that cater to specific languages (English, French, German, etc.).
In this section we'll install and setup spaCy to work with Python, and then introduce some concepts related to Natural Language Processing.
Installation is a two-step process. First, install spaCy using either conda or pip. Next, download the specific model you want, based on language.
For more info visit https://spacy.io/usage/
conda install -c conda-forge spacy
or
pip install -U spacyAlternatively you can create a virtual environment:
conda create -n spacyenv python=3 spacy=2
python -m spacy download enIf successful, you should see a message like:
Linking successful
C:\Anaconda3\envs\spacyenv\lib\site-packages\en_core_web_sm -->
C:\Anaconda3\envs\spacyenv\lib\site-packages\spacy\data\en
`<br>You can now load the model via spacy.load('en')`
In [1]:
# Import spaCy and load the language library
import spacy
nlp = spacy.load('en_core_web_sm')
# Create a Doc object
doc = nlp(u'Tesla is looking at buying U.S. startup for $6 million')
# Print each token separately
for token in doc:
print(token.text, token.pos_, token.dep_)
This doesn't look very user-friendly, but right away we see some interesting things happen:
As we dive deeper into spaCy we'll see what each of these abbreviations mean and how they're derived. We'll also see how spaCy can interpret the last three tokens combined $6 million as referring to money.
After importing the spacy module in the cell above we loaded a model and named it nlp.
Next we created a Doc object by applying the model to our text, and named it doc.
spaCy also builds a companion Vocab object that we'll cover in later sections.
The Doc object that holds the processed text is our focus here.
When we run nlp, our text enters a processing pipeline that first breaks down the text and then performs a series of operations to tag, parse and describe the data. Image source: https://spacy.io/usage/spacy-101#pipelines
We can check to see what components currently live in the pipeline. In later sections we'll learn how to disable components and add new ones as needed.
In [2]:
nlp.pipeline
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nlp.pipe_names
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The first step in processing text is to split up all the component parts (words & punctuation) into "tokens". These tokens are annotated inside the Doc object to contain descriptive information. We'll go into much more detail on tokenization in an upcoming lecture. For now, let's look at another example:
In [4]:
doc2 = nlp(u"Tesla isn't looking into startups anymore.")
for token in doc2:
print(token.text, token.pos_, token.dep_)
Notice how isn't has been split into two tokens. spaCy recognizes both the root verb is and the negation attached to it. Notice also that both the extended whitespace and the period at the end of the sentence are assigned their own tokens.
It's important to note that even though doc2 contains processed information about each token, it also retains the original text:
In [5]:
doc2
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In [6]:
doc2[0]
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In [7]:
type(doc2)
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The next step after splitting the text up into tokens is to assign parts of speech. In the above example, Tesla was recognized to be a proper noun. Here some statistical modeling is required. For example, words that follow "the" are typically nouns.
For a full list of POS Tags visit https://spacy.io/api/annotation#pos-tagging
In [8]:
doc2[0].pos_
Out[8]:
We also looked at the syntactic dependencies assigned to each token. Tesla is identified as an nsubj or the nominal subject of the sentence.
For a full list of Syntactic Dependencies visit https://spacy.io/api/annotation#dependency-parsing
A good explanation of typed dependencies can be found here
In [9]:
doc2[0].dep_
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To see the full name of a tag use spacy.explain(tag)
In [10]:
spacy.explain('PROPN')
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In [11]:
spacy.explain('nsubj')
Out[11]:
| Tag | Description | doc2[0].tag |
|---|---|---|
.text |
The original word text | Tesla |
.lemma_ |
The base form of the word | tesla |
.pos_ |
The simple part-of-speech tag | PROPN/proper noun |
.tag_ |
The detailed part-of-speech tag | NNP/noun, proper singular |
.shape_ |
The word shape – capitalization, punctuation, digits | Xxxxx |
.is_alpha |
Is the token an alpha character? | True |
.is_stop |
Is the token part of a stop list, i.e. the most common words of the language? | False |
In [12]:
# Lemmas (the base form of the word):
print(doc2[4].text)
print(doc2[4].lemma_)
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# Simple Parts-of-Speech & Detailed Tags:
print(doc2[4].pos_)
print(doc2[4].tag_ + ' / ' + spacy.explain(doc2[4].tag_))
In [14]:
# Word Shapes:
print(doc2[0].text+': '+doc2[0].shape_)
print(doc[5].text+' : '+doc[5].shape_)
In [15]:
# Boolean Values:
print(doc2[0].is_alpha)
print(doc2[0].is_stop)
In [16]:
doc3 = nlp(u'Although commmonly attributed to John Lennon from his song "Beautiful Boy", \
the phrase "Life is what happens to us while we are making other plans" was written by \
cartoonist Allen Saunders and published in Reader\'s Digest in 1957, when Lennon was 17.')
In [17]:
life_quote = doc3[16:30]
print(life_quote)
In [18]:
type(life_quote)
Out[18]:
In upcoming lectures we'll see how to create Span objects using Span(). This will allow us to assign additional information to the Span.
In [19]:
doc4 = nlp(u'This is the first sentence. This is another sentence. This is the last sentence.')
In [20]:
for sent in doc4.sents:
print(sent)
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doc4[6].is_sent_start
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