mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-10 19:57:17 +03:00
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
|
||
# coding: utf8
|
||
"""A simple example of extracting relations between phrases and entities using
|
||
spaCy's named entity recognizer and the dependency parse. Here, we extract
|
||
money and currency values (entities labelled as MONEY) and then check the
|
||
dependency tree to find the noun phrase they are referring to – for example:
|
||
$9.4 million --> Net income.
|
||
|
||
Compatible with: spaCy v2.0.0+
|
||
Last tested with: v2.2.1
|
||
"""
|
||
from __future__ import unicode_literals, print_function
|
||
|
||
import plac
|
||
import spacy
|
||
|
||
|
||
TEXTS = [
|
||
"Net income was $9.4 million compared to the prior year of $2.7 million.",
|
||
"Revenue exceeded twelve billion dollars, with a loss of $1b.",
|
||
]
|
||
|
||
|
||
@plac.annotations(
|
||
model=("Model to load (needs parser and NER)", "positional", None, str)
|
||
)
|
||
def main(model="en_core_web_sm"):
|
||
nlp = spacy.load(model)
|
||
print("Loaded model '%s'" % model)
|
||
print("Processing %d texts" % len(TEXTS))
|
||
|
||
for text in TEXTS:
|
||
doc = nlp(text)
|
||
relations = extract_currency_relations(doc)
|
||
for r1, r2 in relations:
|
||
print("{:<10}\t{}\t{}".format(r1.text, r2.ent_type_, r2.text))
|
||
|
||
|
||
def filter_spans(spans):
|
||
# Filter a sequence of spans so they don't contain overlaps
|
||
# For spaCy 2.1.4+: this function is available as spacy.util.filter_spans()
|
||
get_sort_key = lambda span: (span.end - span.start, -span.start)
|
||
sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
|
||
result = []
|
||
seen_tokens = set()
|
||
for span in sorted_spans:
|
||
# Check for end - 1 here because boundaries are inclusive
|
||
if span.start not in seen_tokens and span.end - 1 not in seen_tokens:
|
||
result.append(span)
|
||
seen_tokens.update(range(span.start, span.end))
|
||
result = sorted(result, key=lambda span: span.start)
|
||
return result
|
||
|
||
|
||
def extract_currency_relations(doc):
|
||
# Merge entities and noun chunks into one token
|
||
spans = list(doc.ents) + list(doc.noun_chunks)
|
||
spans = filter_spans(spans)
|
||
with doc.retokenize() as retokenizer:
|
||
for span in spans:
|
||
retokenizer.merge(span)
|
||
|
||
relations = []
|
||
for money in filter(lambda w: w.ent_type_ == "MONEY", doc):
|
||
if money.dep_ in ("attr", "dobj"):
|
||
subject = [w for w in money.head.lefts if w.dep_ == "nsubj"]
|
||
if subject:
|
||
subject = subject[0]
|
||
relations.append((subject, money))
|
||
elif money.dep_ == "pobj" and money.head.dep_ == "prep":
|
||
relations.append((money.head.head, money))
|
||
return relations
|
||
|
||
|
||
if __name__ == "__main__":
|
||
plac.call(main)
|
||
|
||
# Expected output:
|
||
# Net income MONEY $9.4 million
|
||
# the prior year MONEY $2.7 million
|
||
# Revenue MONEY twelve billion dollars
|
||
# a loss MONEY 1b
|