mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-31 07:57:35 +03:00
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
from spacy.kb import KnowledgeBase
|
|
from spacy.lang.en import English
|
|
from spacy.training import Example
|
|
|
|
|
|
def test_issue7065():
|
|
text = "Kathleen Battle sang in Mahler 's Symphony No. 8 at the Cincinnati Symphony Orchestra 's May Festival."
|
|
nlp = English()
|
|
nlp.add_pipe("sentencizer")
|
|
ruler = nlp.add_pipe("entity_ruler")
|
|
patterns = [{"label": "THING", "pattern": [{"LOWER": "symphony"}, {"LOWER": "no"}, {"LOWER": "."}, {"LOWER": "8"}]}]
|
|
ruler.add_patterns(patterns)
|
|
|
|
doc = nlp(text)
|
|
sentences = [s for s in doc.sents]
|
|
assert len(sentences) == 2
|
|
sent0 = sentences[0]
|
|
ent = doc.ents[0]
|
|
assert ent.start < sent0.end < ent.end
|
|
assert sentences.index(ent.sent) == 0
|
|
|
|
|
|
def test_issue7065_b():
|
|
# Test that the NEL doesn't crash when an entity crosses a sentence boundary
|
|
nlp = English()
|
|
vector_length = 3
|
|
nlp.add_pipe("sentencizer")
|
|
|
|
text = "Mahler 's Symphony No. 8 was beautiful."
|
|
entities = [(0, 6, "PERSON"), (10, 24, "WORK")]
|
|
links = {(0, 6): {"Q7304": 1.0, "Q270853": 0.0},
|
|
(10, 24): {"Q7304": 0.0, "Q270853": 1.0}}
|
|
sent_starts = [1, -1, 0, 0, 0, 0, 0, 0, 0]
|
|
doc = nlp(text)
|
|
example = Example.from_dict(doc, {"entities": entities, "links": links, "sent_starts": sent_starts})
|
|
train_examples = [example]
|
|
|
|
def create_kb(vocab):
|
|
# create artificial KB
|
|
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
|
|
mykb.add_entity(entity="Q270853", freq=12, entity_vector=[9, 1, -7])
|
|
mykb.add_alias(
|
|
alias="No. 8",
|
|
entities=["Q270853"],
|
|
probabilities=[1.0],
|
|
)
|
|
mykb.add_entity(entity="Q7304", freq=12, entity_vector=[6, -4, 3])
|
|
mykb.add_alias(
|
|
alias="Mahler",
|
|
entities=["Q7304"],
|
|
probabilities=[1.0],
|
|
)
|
|
return mykb
|
|
|
|
# Create the Entity Linker component and add it to the pipeline
|
|
entity_linker = nlp.add_pipe("entity_linker", last=True)
|
|
entity_linker.set_kb(create_kb)
|
|
|
|
# train the NEL pipe
|
|
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
|
for i in range(2):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
|
|
# Add a custom rule-based component to mimick NER
|
|
patterns = [
|
|
{"label": "PERSON", "pattern": [{"LOWER": "mahler"}]},
|
|
{"label": "WORK", "pattern": [{"LOWER": "symphony"}, {"LOWER": "no"}, {"LOWER": "."}, {"LOWER": "8"}]}
|
|
]
|
|
ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
|
|
ruler.add_patterns(patterns)
|
|
|
|
# test the trained model - this should not throw E148
|
|
doc = nlp(text)
|
|
assert doc
|