def test_split_sentences_by_char_limit():
"""Unit test of splitting sentences by char limit."""
lingual_parser = SpacyParser("en")
text = "This is a text. This is another text."
all_sentences = [
Sentence(**parts) for parts in lingual_parser.split_sentences(text)
]
assert len(all_sentences) == 2
assert [len(sentence.text) for sentence in all_sentences] == [15, 21]
lingual_parser.model.remove_pipe("sentencizer")
lingual_parser.model.add_pipe(set_custom_boundary,
before="parser",
name="sentence_boundary_detector")
sentence_batches = lingual_parser._split_sentences_by_char_limit(
all_sentences, 20)
assert len(sentence_batches) == 2
sentence_batches = lingual_parser._split_sentences_by_char_limit(
all_sentences, 100)
assert len(sentence_batches) == 1
sentence_batch = sentence_batches[0]
custom_tokenizer = TokenPreservingTokenizer(lingual_parser.model.vocab)
doc = custom_tokenizer(sentence_batch)
doc.user_data = sentence_batch
for name, proc in lingual_parser.model.pipeline: # iterate over components in order
doc = proc(doc)
assert doc.is_parsed
# See if the number of parsed spaCy sentences matches that of input sentences
assert len(list(doc.sents)) == len(sentence_batch)
def doc_setup():
doc = Document(id=1, name="test", stable_id="1::document:0:0")
doc.text = "This is apple"
lingual_parser = SpacyParser("en")
for parts in lingual_parser.split_sentences(doc.text):
parts["document"] = doc
Sentence(**parts)
return doc
def doc_setup():
"""Set up document."""
doc = Document(id=1, name="test", stable_id="1::document:0:0")
doc.text = "This is apple. That is orange. Where is banaba? I like Apple."
lingual_parser = SpacyParser("en")
# Split sentences
for parts in lingual_parser.split_sentences(doc.text):
parts["document"] = doc
Sentence(**parts)
# Enrich sentences
for _ in lingual_parser.enrich_sentences_with_NLP(doc.sentences):
pass
# Pick one sentence and add visual information
# so that all the words get aligned horizontally.
sentence: Sentence = doc.sentences[0]
sentence.page = [1, 1, 1, 1]
sentence.top = [0, 0, 0, 0]
sentence.bottom = [10, 10, 10, 10]
sentence.left = [0, 10, 20, 30]
sentence.right = [10, 20, 30, 40]
# Assume the 2nd sentence is horizontally aligned with 1st.
sentence: Sentence = doc.sentences[1]
sentence.page = [1, 1, 1, 1]
sentence.top = [0, 0, 0, 0]
sentence.bottom = [10, 10, 10, 10]
sentence.left = [40, 50, 60, 70]
sentence.right = [50, 60, 70, 80]
# Assume the 3rd sentence is vertically aligned with 1st.
sentence: Sentence = doc.sentences[2]
sentence.page = [1, 1, 1, 1]
sentence.top = [10, 10, 10, 10]
sentence.bottom = [20, 20, 20, 20]
sentence.left = [0, 10, 20, 30]
sentence.right = [10, 20, 30, 40]
# Assume the 4th sentence is in 2nd page.
sentence: Sentence = doc.sentences[3]
sentence.page = [2, 2, 2, 2]
sentence.top = [0, 0, 0, 0]
sentence.bottom = [10, 10, 10, 10]
sentence.left = [0, 10, 20, 30]
sentence.right = [10, 20, 30, 40]
return doc
def test_ner_matchers():
"""Test different ner type matchers."""
# Set up a document
doc = Document(id=1, name="test", stable_id="1::document:0:0")
doc.text = " ".join([
"Tim Cook was born in USA in 1960.",
"He is the CEO of Apple.",
"He sold 100 million of iPhone.",
])
lingual_parser = SpacyParser("en")
for parts in lingual_parser.split_sentences(doc.text):
parts["document"] = doc
Sentence(**parts)
# Manually attach ner_tags as the result from spacy may fluctuate.
doc.sentences[0].ner_tags = [
"PERSON",
"PERSON",
"O",
"O",
"O",
"GPE",
"O",
"DATE",
"O",
]
doc.sentences[1].ner_tags = ["O", "O", "O", "O", "O", "ORG", "O"]
doc.sentences[2].ner_tags = [
"O", "O", "CARDINAL", "CARDINAL", "O", "MISC", "O"
]
# the length of words and that of ner_tags should match.
assert len(doc.sentences[0].words) == len(doc.sentences[0].ner_tags)
assert len(doc.sentences[1].words) == len(doc.sentences[1].ner_tags)
space = MentionNgrams(n_min=1, n_max=2)
# Test if PersonMatcher works as expected
matcher = PersonMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"Tim Cook"}
# Test if LocationMatcher works as expected
matcher = LocationMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"USA"}
# Test if DateMatcher works as expected
matcher = DateMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"1960"}
# Test if OrganizationMatcher works as expected
matcher = OrganizationMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"Apple"}
# Test if NumberMatcher works as expected
matcher = NumberMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"100 million"}
# Test if MiscMatcher works as expected
matcher = MiscMatcher()
assert set(tc.get_span()
for tc in matcher.apply(space.apply(doc))) == {"iPhone"}