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63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""A simple example of extracting relations between phrases and entities using
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spaCy's named entity recognizer and the dependency parse. Here, we extract
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money and currency values (entities labelled as MONEY) and then check the
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dependency tree to find the noun phrase they are referring to – for example:
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$9.4 million --> Net income.
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Compatible with: spaCy v2.0.0+
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"""
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from __future__ import unicode_literals, print_function
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import plac
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import spacy
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TEXTS = [
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'Net income was $9.4 million compared to the prior year of $2.7 million.',
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'Revenue exceeded twelve billion dollars, with a loss of $1b.',
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]
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@plac.annotations(
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model=("Model to load (needs parser and NER)", "positional", None, str))
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def main(model='en_core_web_sm'):
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nlp = spacy.load(model)
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print("Loaded model '%s'" % model)
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print("Processing %d texts" % len(TEXTS))
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for text in TEXTS:
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doc = nlp(text)
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relations = extract_currency_relations(doc)
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for r1, r2 in relations:
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print('{:<10}\t{}\t{}'.format(r1.text, r2.ent_type_, r2.text))
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def extract_currency_relations(doc):
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# merge entities and noun chunks into one token
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spans = list(doc.ents) + list(doc.noun_chunks)
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for span in spans:
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span.merge()
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relations = []
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for money in filter(lambda w: w.ent_type_ == 'MONEY', doc):
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if money.dep_ in ('attr', 'dobj'):
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subject = [w for w in money.head.lefts if w.dep_ == 'nsubj']
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if subject:
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subject = subject[0]
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relations.append((subject, money))
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elif money.dep_ == 'pobj' and money.head.dep_ == 'prep':
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relations.append((money.head.head, money))
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return relations
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if __name__ == '__main__':
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plac.call(main)
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# Expected output:
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# Net income MONEY $9.4 million
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# the prior year MONEY $2.7 million
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# Revenue MONEY twelve billion dollars
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# a loss MONEY 1b
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