def test_noun_chunks_min_freq(spacy_doc):
expected = ['I', 'we', 'he', 'I', 'we', 'he', 'we']
observed = [
nc.text for nc in extract.noun_chunks(
spacy_doc, drop_determiners=True, min_freq=2)
]
assert observed == expected
def test_noun_chunks_min_freq(self):
expected = ['I', 'we', 'he', 'I', 'we', 'he', 'we']
observed = [
nc.text for nc in extract.noun_chunks(
self.spacy_doc, drop_determiners=True, min_freq=2)
]
self.assertEqual(observed, expected)
def select_extractors(use_unigrams=False):
"""
Extractors For Alignment
:return: List of Extractors objects to use for text-text alignment
note: ngram extractors below filter out stopwords and number words/symbols
"""
noun_chunk_extractor = Extractor(
lambda doc: list(filter(lambda x: len(x) > 3, list(noun_chunks(doc)))))
tetragram_extractor = Extractor(
lambda doc: list(ngrams(doc, 4, filter_stops=True, filter_nums=True)))
trigram_extractor = Extractor(
lambda doc: list(ngrams(doc, 3, filter_stops=True, filter_nums=True)))
bigram_extractor = Extractor(lambda doc: list(
ngrams(doc, 2, filter_stops=False, filter_nums=False)))
unigram_extractor = Extractor(lambda doc: list(
ngrams(doc, 1, filter_stops=False, filter_nums=False)))
extractor_list = [
noun_chunk_extractor,
tetragram_extractor,
trigram_extractor,
bigram_extractor,
]
if use_unigrams:
extractor_list.append(unigram_extractor)
return extractor_list
def noun_chunks(self, **kwargs):
"""
Extract an ordered sequence of noun phrases from doc, optionally
filtering by frequency and dropping leading determiners.
.. seealso:: :func:`extract.noun_chunks() <textacy.extract.noun_chunks>`
for all function kwargs.
"""
return extract.noun_chunks(self.spacy_doc, **kwargs)
def test_noun_chunks(spacy_doc):
expected = [
'I', 'Kuwait', 'I.M.F. seminar', 'Arab educators', '30 minutes', 'we',
'impact', 'technology trends', 'education', 'Middle East'
]
observed = [
nc.text for nc in extract.noun_chunks(spacy_doc, drop_determiners=True)
][:10]
assert observed == expected
def noun_chunks(self, **kwargs):
"""
Extract an ordered sequence of noun phrases from doc, optionally
filtering by frequency and dropping leading determiners.
.. seealso:: :func:`extract.noun_chunks() <textacy.extract.noun_chunks>`
for all function kwargs.
"""
return extract.noun_chunks(self.spacy_doc, **kwargs)
def test_noun_chunks(self):
expected = [
'I', 'Kuwait', 'I.M.F. seminar', 'Arab educators', '30 minutes', 'we',
'impact', 'technology trends', 'education', 'Middle East', 'Egyptian education official',
'his hand', 'he', 'personal question', 'I', 'Donald Trump', 'we', 'mosques',
'United States', 'he', 'great sorrow', 'what', 'we', 'our kids']
observed = [nc.text for nc in extract.noun_chunks(
self.spacy_doc, drop_determiners=True)]
self.assertEqual(observed, expected)
def test_noun_chunks_determiner(self):
expected = [
'I', 'Kuwait', 'an I.M.F. seminar', 'Arab educators', '30 minutes',
'we', 'the impact', 'technology trends', 'education',
'the Middle East', 'an Egyptian education official', 'his hand',
'he', 'a personal question', 'I', 'Donald Trump', 'we', 'mosques',
'the United States', 'he', 'great sorrow', 'what', 'we', 'our kids'
]
observed = [
nc.text for nc in extract.noun_chunks(self.spacy_doc,
drop_determiners=False)
]
self.assertEqual(observed, expected)
def noun_chunks(self, **kwargs):
"""
Extract an ordered sequence of noun phrases from doc, optionally
filtering by frequency and dropping leading determiners.
Args:
**kwargs:
drop_determiners (bool, optional): remove leading determiners (e.g. "the")
from phrases (e.g. "the quick brown fox" => "quick brown fox")
min_freq (int, optional): remove chunks that occur in `doc` fewer than
`min_freq` times
Yields:
``spacy.Span``: the next noun chunk, in order of appearance in the document
.. seealso:: :func:`extract.noun_chunks() <textacy.extract.noun_chunks>`
"""
for nc in extract.noun_chunks(self.spacy_doc, **kwargs):
yield nc
def noun_chunks(self, **kwargs):
"""
Extract an ordered sequence of noun phrases from doc, optionally
filtering by frequency and dropping leading determiners.
Args:
**kwargs:
drop_determiners (bool, optional): remove leading determiners (e.g. "the")
from phrases (e.g. "the quick brown fox" => "quick brown fox")
min_freq (int, optional): remove chunks that occur in `doc` fewer than
`min_freq` times
Yields:
``spacy.Span``: the next noun chunk, in order of appearance in the document
.. seealso:: :func:`extract.noun_chunks() <textacy.extract.noun_chunks>`
"""
for nc in extract.noun_chunks(self.spacy_doc, **kwargs):
yield nc
def grammars( carrel, grammar, query, noun, lemma, sort, count ) :
"""Extract sentence fragments from <carrel> where fragments are one of:
\b
nouns - all the nouns and noun chunks
quotes - things people say
svo - fragments in the form of subject-verb-object (the default)
sss - a more advanced version of svo; fragments beginning
with an entity, are co-occur with a verb, and are followed
by a phrase
This is very useful for the purposes of listing more complete ideas from a text.
Examples:
\b
rdr grammars homer
rdr grammars -g nouns homer
rdr grammars -g sss -n hector -l be homer"""
# require
from textacy import extract
from os import system
from re import search
# sanity check
checkForCarrel( carrel )
# initialize
doc = carrel2doc( carrel )
# get the features; svo
if grammar == 'svo' :
# do the work
features = list( extract.subject_verb_object_triples( doc ) )
# simplify the result
items = []
for feature in features :
subject = [ token.text_with_ws for token in feature.subject ]
verb = [ token.text_with_ws for token in feature.verb ]
object = [ token.text_with_ws for token in feature.object ]
items.append(' \t'.join( [ ''.join( subject ), ''.join( verb ), ''.join( object ) ] ) )
# done
features = items
# quotes
elif grammar == 'quotes' :
# do the work
features = list( extract.direct_quotations( doc ) )
# simplify the result
items = []
for feature in features :
# parse and stringify
speaker = [ token.text_with_ws for token in feature.speaker ]
cue = [ token.text_with_ws for token in feature.cue ]
content = feature.content.text_with_ws
items.append( '\t'.join( [ ''.join( speaker ), ''.join( cue ), content ] ) )
# done
features = items
# noun chunks
elif grammar == 'nouns' :
# do the work and simplify the result
features = list( extract.noun_chunks( doc ) )
features = [ feature.text for feature in features ]
# semi-structured sentences
elif grammar == 'sss' :
# sanity check
if not noun :
click.echo( "Error: When specifying sss, the -n option is required. See 'rdr grammars --help'.", err=True )
exit()
# do the work
features = list( extract.semistructured_statements( doc, entity=noun, cue=lemma ) )
# simplify the result
items = []
for feature in features :
entity = [ token.text_with_ws for token in feature.entity ]
cue = [ token.text_with_ws for token in feature.cue ]
fragment = [ token.text_with_ws for token in feature.fragment ]
items.append( '\t'.join( [ ''.join( entity ), ''.join( cue ), ''.join( fragment ) ] ) )
# done
features = items
# filter, conditionally
if query : features = [ feature for feature in features if ( search( query, feature ) ) ]
# sort, conditionally
if sort : features.sort()
# count, conditionally
if count :
# initialize a dictionary and process each feature
items = {}
for feature in features :
# update the dictionary
if feature in items : items[ feature ] += 1
else : items[ feature ] = 1
# sort the dictionary; return the features
features = sorted( items.items(), key=lambda x:x[ 1 ], reverse=True )
# process each feature, again
items = []
for feature in features :
# create a record and update
record = str( feature[ 1 ] ) + '\t' + feature[ 0 ]
items.append( record )
# done
features = items
# output
for feature in features : click.echo( feature )
def test_determiner(self, spacy_doc):
result = list(extract.noun_chunks(spacy_doc, drop_determiners=False))
assert all(isinstance(span, Span) for span in result)
assert any(span[0].pos_ == "DET" for span in result)
def test_default(self, spacy_doc):
result = list(extract.noun_chunks(spacy_doc))
assert all(isinstance(span, Span) for span in result)
def extract_noun_chunks(doc, min_freq=1):
return extract.noun_chunks(doc, drop_determiners=True, min_freq=min_freq)
def test_min_freq(self, spacy_doc):
text = spacy_doc.text.lower()
result = list(extract.noun_chunks(spacy_doc, drop_determiners=True, min_freq=2))
assert all(text.count(span.lower_) >= 2 for span in result)
def test_noun_chunks_min_freq(self):
expected = ['I', 'we', 'he', 'I', 'we', 'he', 'we']
observed = [nc.text for nc in extract.noun_chunks(
self.spacy_doc, drop_determiners=True, min_freq=2)]
self.assertEqual(observed, expected)
def noun_chunks(s):
s = unicode(s)
doc = nlp(s)
return list(extr.noun_chunks(doc))