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https://github.com/explosion/spaCy.git
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6f54e59fe7
* Update util.filter_spans() to prefer earlier spans * Add filter_spans test for first same-length span * Update entity relation example to refer to util.filter_spans()
83 lines
2.7 KiB
Python
83 lines
2.7 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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Last tested with: v2.2.1
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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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)
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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 filter_spans(spans):
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# Filter a sequence of spans so they don't contain overlaps
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# For spaCy 2.1.4+: this function is available as spacy.util.filter_spans()
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get_sort_key = lambda span: (span.end - span.start, -span.start)
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sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
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result = []
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seen_tokens = set()
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for span in sorted_spans:
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# Check for end - 1 here because boundaries are inclusive
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if span.start not in seen_tokens and span.end - 1 not in seen_tokens:
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result.append(span)
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seen_tokens.update(range(span.start, span.end))
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result = sorted(result, key=lambda span: span.start)
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return result
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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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spans = filter_spans(spans)
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with doc.retokenize() as retokenizer:
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for span in spans:
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retokenizer.merge(span)
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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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