pynlp

A pythonic wrapper for Stanford CoreNLP.

Description

This library provides a Python interface to Stanford CoreNLP built over corenlp_protobuf.

Installation

1. Download Stanford CoreNLP from the official download page.
2. Unzip the file and set your CORE_NLP environment variable to point to the directory.
3. Install pynlp from pip

pip3 install pynlp

Quick Start

Launch the server

Lauch the StanfordCoreNLPServer using the instruction given here. Alternatively, simply run the module.

python3 -m pynlp

By default, this lauches the server on localhost using port 9000 and 4gb ram for the JVM. Use the --help option for instruction on custom configurations.

Example

Let's start off with an excerpt from a CNN article.

text = ('GOP Sen. Rand Paul was assaulted in his home in Bowling Green, Kentucky, on Friday, '
        'according to Kentucky State Police. State troopers responded to a call to the senator\'s '
        'residence at 3:21 p.m. Friday. Police arrested a man named Rene Albert Boucher, who they '
        'allege "intentionally assaulted" Paul, causing him "minor injury". Boucher, 59, of Bowling '
        'Green was charged with one count of fourth-degree assault. As of Saturday afternoon, he '
        'was being held in the Warren County Regional Jail on a $5,000 bond.')

Instantiate annotator

Here we demonstrate the following annotators:

• Annotoators: tokenize, ssplit, pos, lemma, ner, entitymentions, coref, sentiment, quote, openie
• Options: openie.resolve_coref

from pynlp import StanfordCoreNLP

annotators = 'tokenize, ssplit, pos, lemma, ner, entitymentions, coref, sentiment, quote, openie'
options = {'openie.resolve_coref': True}

nlp = StanfordCoreNLP(annotators=annotators, options=options)

Annotate text

The nlp instance is callable. Use it to annotate the text and return a Document object.

document = nlp(text)

print(document) # prints 'text'

Sentence splitting

Let's test the ssplit annotator. A Document object iterates over its Sentence objects.

for index, sentence in enumerate(document):
    print(index, sentence, sep=' )')

Output:

0) GOP Sen. Rand Paul was assaulted in his home in Bowling Green, Kentucky, on Friday, according to Kentucky State Police.
1) State troopers responded to a call to the senator's residence at 3:21 p.m. Friday.
2) Police arrested a man named Rene Albert Boucher, who they allege "intentionally assaulted" Paul, causing him "minor injury".
3) Boucher, 59, of Bowling Green was charged with one count of fourth-degree assault.
4) As of Saturday afternoon, he was being held in the Warren County Regional Jail on a $5,000 bond.

Named entity recognition

How about finding all the people mentioned in the document?

[str(entity) for entity in document.entities if entity.type == 'PERSON']

Output:

Out[2]: ['Rand Paul', 'Rene Albert Boucher', 'Paul', 'Boucher']

We may use named entities on a sentence level too.

first_sentence = document[0]
for entity in first_sentence.entities:
    print(entity, '({})'.format(entity.type))

Output:

GOP (ORGANIZATION)
Rand Paul (PERSON)
Bowling Green (LOCATION)
Kentucky (LOCATION)
Friday (DATE)
Kentucky State Police (ORGANIZATION)

Part-of-speech tagging

Let's find all the 'VB' tags in the first sentence. A Sentence object iterates over Token objects.

for token in first_sentence:
    if 'VB' in token.pos:
        print(token, token.pos)

Output:

was VBD
assaulted VBN
according VBG

Lemmatization

Using the same words, lets see the lemmas.

for token in first_sentence:
    if 'VB' in token.pos:
       print(token, '->', token.lemma)

Output:

was -> be
assaulted -> assault
according -> accord

Coreference resultion

Let's use pynlp to find the first CorefChain in the text.

chain = document.coref_chains[0]
print(chain)

Output:

((GOP Sen. Rand Paul))-[id=4] was assaulted in (his)-[id=5] home in Bowling Green, Kentucky, on Friday, according to Kentucky State Police.
State troopers responded to a call to (the senator's)-[id=10] residence at 3:21 p.m. Friday.
Police arrested a man named Rene Albert Boucher, who they allege "(intentionally assaulted" Paul)-[id=16], causing him "minor injury.

In the string representation, coreferences are marked with parenthesis and the referent with double parenthesis. Each is also labelled with a coref_id. Let's have a closer look at the referent.

ref = chain.referent
print('Coreference: {}\n'.format(ref))

for attr in 'type', 'number', 'animacy', 'gender':
    print(attr,  getattr(ref, attr), sep=': ')

# Note that we can also index coreferences by id
assert chain[4].is_referent

Output:

Coreference: Police

type: PROPER
number: SINGULAR
animacy: ANIMATE
gender: UNKNOWN

Quotes

Extracting quotes from the text is simple.

print(document.quotes)

Output:

[<Quote: "intentionally assaulted">, <Quote: "minor injury">]

TODO (annotation wrappers):

• ssplit
• ner
• pos
• lemma
• coref
• quote
• quote.attribution
• parse
• depparse
• entitymentions
• openie
• sentiment
• relation
• kbp
• entitylink
• 'options' examples i.e openie.resolve_coref

Saving annotations

Write

A pynlp document can be saved as a byte string.

with open('annotation.dat', 'wb') as file:
    file.write(document.to_bytes())

Read

To load a pynlp document, instantiate a Document with the from_bytes class method.

from pynlp import Document

with open('annotation.dat', 'rb') as file:
    document = Document.from_bytes(file.read())