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60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
import plac
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from spacy.en import English
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from spacy.parts_of_speech import NOUN
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from spacy.parts_of_speech import ADP as PREP
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def _span_to_tuple(span):
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start = span[0].idx
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end = span[-1].idx + len(span[-1])
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tag = span.root.tag_
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text = span.text
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label = span.label_
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return (start, end, tag, text, label)
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def merge_spans(spans, doc):
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# This is a bit awkward atm. What we're doing here is merging the entities,
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# so that each only takes up a single token. But an entity is a Span, and
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# each Span is a view into the doc. When we merge a span, we invalidate
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# the other spans. This will get fixed --- but for now the solution
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# is to gather the information first, before merging.
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tuples = [_span_to_tuple(span) for span in spans]
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for span_tuple in tuples:
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doc.merge(*span_tuple)
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def extract_currency_relations(doc):
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merge_spans(doc.ents, doc)
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merge_spans(doc.noun_chunks, doc)
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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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def main():
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nlp = English()
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texts = [
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u'Net income was $9.4 million compared to the prior year of $2.7 million.',
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u'Revenue exceeded twelve billion dollars, with a loss of $1b.',
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]
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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(r1.text, r2.ent_type_, r2.text)
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if __name__ == '__main__':
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plac.call(main)
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