def simple_subjects_and_objects(self, verb):
"""
Extract all simple subjects and objects for a given verb.
Uses Textacy get_objects_of_verb and get_subjects_of_verb methods
param: verb A Spacy Token
return: A list of verb subjects and objects (Spacy Tokens or Spans)
"""
verb_objects = get_objects_of_verb(verb)
verb_subjects = get_subjects_of_verb(verb)
verb_objects.extend(verb_subjects)
return verb_objects
def subject_verb_object_triples(doc):
"""
Extract an ordered sequence of subject-verb-object (SVO) triples from a
spacy-parsed doc. Note that this only works for SVO languages.
Args:
doc (``spacy.Doc`` or ``spacy.Span``): either a spacy document
or a sentence thereof
Yields:
(``spacy.Span``, ``spacy.Span``, ``spacy.Span``): the next 3-tuple from ``doc``
representing a (subject, verb, object) triple, in order of apperance
# TODO: What to do about questions, where it may be VSO instead of SVO?
# TODO: What about non-adjacent verb negations?
# TODO: What about object (noun) negations?
"""
try:
sents = doc.sents
except AttributeError:
sents = [doc]
for sent in sents:
start_i = sent[0].i
verbs = get_main_verbs_of_sent(sent)
for verb in verbs:
subjs = get_subjects_of_verb(verb)
if not subjs:
continue
objs = get_objects_of_verb(verb)
if not objs:
continue
# add adjacent auxiliaries to verbs, for context
# and add compounds to compound nouns
verb_span = get_span_for_verb_auxiliaries(verb)
verb = sent[verb_span[0] - start_i: verb_span[1] - start_i + 1]
for subj in subjs:
subj = sent[get_span_for_compound_noun(subj)[0] - start_i: subj.i - start_i + 1]
for obj in objs:
if obj.pos == NOUN:
span = get_span_for_compound_noun(obj)
elif obj.pos == VERB:
span = get_span_for_verb_auxiliaries(obj)
else:
span = (obj.i, obj.i)
obj = sent[span[0] - start_i: span[1] - start_i + 1]
yield (subj, verb, obj)
def direct_quotations(doc):
"""
Baseline, not-great attempt at direction quotation extraction (no indirect
or mixed quotations) using rules and patterns. English only.
Args:
doc (``textacy.Doc`` or ``spacy.Doc``)
Yields:
(``spacy.Span``, ``spacy.Token``, ``spacy.Span``): next quotation in ``doc``
represented as a (speaker, reporting verb, quotation) 3-tuple
Notes:
Loosely inspired by Krestel, Bergler, Witte. "Minding the Source: Automatic
Tagging of Reported Speech in Newspaper Articles".
TODO: Better approach would use ML, but needs a training dataset.
"""
if isinstance(doc, textacy.Doc):
if doc.lang != 'en':
raise NotImplementedError('sorry, English-language texts only :(')
doc = doc.spacy_doc
quote_end_punct = {',', '.', '?', '!'}
quote_indexes = set(itertoolz.concat(
(m.start(), m.end() - 1) for m in re.finditer(r"(\".*?\")|(''.*?'')|(``.*?'')", doc.string)))
quote_positions = list(itertoolz.partition(
2, sorted(tok.i for tok in doc if tok.idx in quote_indexes)))
sents = list(doc.sents)
sent_positions = [(sent.start, sent.end) for sent in sents]
for q0, q1 in quote_positions:
quote = doc[q0: q1 + 1]
# we're only looking for direct quotes, not indirect or mixed
if not any(char in quote_end_punct for char in quote.text[-4:]):
continue
# get adjacent sentences
candidate_sent_indexes = []
for i, (s0, s1) in enumerate(sent_positions):
if s0 <= q1 + 1 and s1 > q1:
candidate_sent_indexes.append(i)
elif s0 < q0 and s1 >= q0 - 1:
candidate_sent_indexes.append(i)
for si in candidate_sent_indexes:
sent = sents[si]
# get any reporting verbs
rvs = [tok for tok in sent
if spacy_utils.preserve_case(tok) is False and
tok.lemma_ in REPORTING_VERBS and
tok.pos_ == 'VERB' and
not any(oq0 <= tok.i <= oq1 for oq0, oq1 in quote_positions)]
# get target offset against which to measure distances of NEs
if rvs:
if len(rvs) == 1:
rv = rvs[0]
else:
min_rv_dist = 1000
for rv_candidate in rvs:
rv_dist = min(abs(rv_candidate.i - qp) for qp in (q0, q1))
if rv_dist < min_rv_dist:
rv = rv_candidate
min_rv_dist = rv_dist
else:
break
else:
# TODO: do we have no other recourse?!
continue
try:
# rv_subj = _find_subjects(rv)[0]
rv_subj = get_subjects_of_verb(rv)[0]
except IndexError:
continue
# if rv_subj.text in {'he', 'she'}:
# for ne in named_entities(doc, good_ne_types={'PERSON'}):
# if ne.start < rv_subj.i:
# speaker = ne
# else:
# break
# else:
span = get_span_for_compound_noun(rv_subj)
speaker = doc[span[0]: span[1] + 1]
yield (speaker, rv, quote)
break
def direct_quotations(doc):
"""
Baseline, not-great attempt at direction quotation extraction (no indirect
or mixed quotations) using rules and patterns. English only.
Args:
doc (``spacy.Doc``)
Yields:
(``spacy.Span``, ``spacy.Token``, ``spacy.Span``): next quotation in ``doc``
represented as a (speaker, reporting verb, quotation) 3-tuple
Notes:
Loosely inspired by Krestel, Bergler, Witte. "Minding the Source: Automatic
Tagging of Reported Speech in Newspaper Articles".
TODO: Better approach would use ML, but needs a training dataset.
"""
quote_end_punct = {',', '.', '?', '!'}
quote_indexes = set(itertoolz.concat(
(m.start(), m.end() - 1) for m in re.finditer(r"(\".*?\")|(''.*?'')|(``.*?'')", doc.string)))
quote_positions = list(itertoolz.partition(
2, sorted(tok.i for tok in doc if tok.idx in quote_indexes)))
sents = list(doc.sents)
sent_positions = [(sent.start, sent.end) for sent in sents]
for q0, q1 in quote_positions:
quote = doc[q0: q1 + 1]
# we're only looking for direct quotes, not indirect or mixed
if not any(char in quote_end_punct for char in quote.text[-4:]):
continue
# get adjacent sentences
candidate_sent_indexes = []
for i, (s0, s1) in enumerate(sent_positions):
if s0 <= q1 + 1 and s1 > q1:
candidate_sent_indexes.append(i)
elif s0 < q0 and s1 >= q0 - 1:
candidate_sent_indexes.append(i)
for si in candidate_sent_indexes:
sent = sents[si]
# get any reporting verbs
rvs = [tok for tok in sent
if spacy_utils.preserve_case(tok) is False
and tok.lemma_ in REPORTING_VERBS
and tok.pos_ == 'VERB'
and not any(oq0 <= tok.i <= oq1 for oq0, oq1 in quote_positions)]
# get target offset against which to measure distances of NEs
if rvs:
if len(rvs) == 1:
rv = rvs[0]
else:
min_rv_dist = 1000
for rv_candidate in rvs:
rv_dist = min(abs(rv_candidate.i - qp) for qp in (q0, q1))
if rv_dist < min_rv_dist:
rv = rv_candidate
min_rv_dist = rv_dist
else:
break
else:
# TODO: do we have no other recourse?!
continue
try:
# rv_subj = _find_subjects(rv)[0]
rv_subj = get_subjects_of_verb(rv)[0]
except IndexError:
continue
# if rv_subj.text in {'he', 'she'}:
# for ne in named_entities(doc, good_ne_types={'PERSON'}):
# if ne.start < rv_subj.i:
# speaker = ne
# else:
# break
# else:
span = get_span_for_compound_noun(rv_subj)
speaker = doc[span[0]: span[1] + 1]
yield (speaker, rv, quote)
break
def subject_verb_object_triples(doc):
"""
Extract an ordered sequence of subject-verb-object (SVO) triples from a
spacy-parsed doc. Note that this only works for SVO languages.
Args:
doc (``textacy.Doc`` or ``spacy.Doc`` or ``spacy.Span``)
Yields:
Tuple[``spacy.Span``, ``spacy.Span``, ``spacy.Span``]: the next 3-tuple
of spans from ``doc`` representing a (subject, verb, object) triple,
in order of appearance
"""
# TODO: Rewrite rules based on http://www.anthology.aclweb.org/W/W12/W12-0702.pdf
# TODO: Think about relative clauses (that-of) e.g., products that include
# TODO: What to do about questions, where it may be VSO instead of SVO?
# TODO: What about non-adjacent verb negations?
# TODO: What about object (noun) negations?
if isinstance(doc, SpacySpan):
sents = [doc]
else: # textacy.Doc or spacy.Doc
sents = doc.sents
for sent in sents:
start_i = sent[0].i
verbs_init = get_main_verbs_of_sent(sent)
list_candidates = []
verb_tmp_token = None
for verb_init in verbs_init:
if (verb_init['token'] != verb_tmp_token):
verb_tmp_token = verb_init['token']
subjs = get_subjects_of_verb(verb_init['token'], sent)
if not subjs:
continue
verbs = get_span_for_verb_auxiliaries(verb_init['token'],
start_i, sent)
list_candidates.append((subjs, verbs))
for subjs, verbs in list_candidates:
for verb in verbs:
objs = get_objects_of_verb(verb['token'])
if not objs:
continue
# add adjacent auxiliaries to verbs, for context
# and add compounds to compound nouns
for subj in subjs:
subj_type = subj.ent_type_
subj = sent[get_span_for_compound_noun(subj)[0] -
start_i:subj.i - start_i + 1]
for obj in objs:
obj_type = obj.ent_type_
if obj.pos != VERB: #obj.pos == NOUN or obj.pos == PROPN:
span = get_span_for_compound_noun(obj)
elif obj.pos == VERB:
#span = get_span_for_verb_auxiliaries(obj, start_i, sent)
span = (obj.i, obj.i)
else:
span = (obj.i, obj.i)
obj = sent[span[0] - start_i:span[1] - start_i + 1]
score = subj.similarity(obj) + obj.similarity(subj)
yield (subj, verb, obj, score, subj_type, obj_type)