def __call__(self, sentence):
m = apply_matcher(sentence.words,
sentence.char_offsets,
self.dictionary,
max_ngrams=self.max_ngrams,
longest_match_only=False,
case_sensitive=self.case_sensitive)
L = {}
for (char_start, char_end), term in m:
if term.lower() in self.stopwords or term in self.stopwords:
continue
start, end = get_word_index_span((char_start, char_end - 1),
sentence)
for i in range(start, end + 1):
L[i] = self.label
return L
def __call__(self, sentence: Sentence) -> Dict[int, int]:
matches = apply_matcher(sentence.words,
sentence.char_offsets,
self.ontology,
max_ngrams=self.max_ngrams,
longest_match_only=True,
case_sensitive=self.case_sensitive)
matches = sorted(matches, key=lambda x: x[0], reverse=0)
L = {}
for (char_start, char_end), term in matches:
label = self._get_term_label(term)
# None labels are treated as abstains
if not label:
continue
start, end = get_word_index_span((char_start, char_end - 1),
sentence)
for i in range(start, end + 1):
L[i] = label
return L
def __call__(self, sentence: Sentence) -> Dict[int, int]:
"""
Parameters
----------
sentence
Returns
-------
"""
matches = apply_matcher(sentence.words,
sentence.char_offsets,
self.ontology,
max_ngrams=self.max_ngrams,
longest_match_only=True,
case_sensitive=self.case_sensitive)
matches = sorted(matches, key=lambda x: x[0], reverse=0)
if not matches:
return {}
matches, labels = self._merge_matches(matches)
terms = [m[-1] for m in matches]
# Slot-filled matches
f_matches = []
mask = np.array([0] * len(matches))
for slot in self.slot_rgxs:
n_args = slot.count('{}')
args = list(zip(terms, labels))
for i in range(len(args) - n_args + 1):
# skip arguments that are already matched
if 1 in mask[i:i + n_args]:
continue
xs, ys = zip(*args[i:i + n_args])
# HACK - positive classes only
if None in ys or 2 in ys:
continue
rgx = re.compile(slot.format(*xs), re.I)
m = match_rgx(rgx, sentence)
if m:
m = list(m.items())[0]
span = list(m[0][0:2])
span[-1] += 1
m = tuple([span, m[-1].text])
# expand the argument matches to this span
mask[i:i + n_args] = 1
f_matches.append((m, np.unique(ys)[0]))
# add slot filled matches
matches = [m for i, m in zip(mask, matches) if i == 0]
labels = [y for i, y in zip(mask, labels) if i == 0]
for m, y in f_matches:
matches.append(m)
labels.append(y)
flip = False
L = {}
for ((char_start, char_end), term), label in zip(matches, labels):
#key = term.lower() if term.lower() in self._labels else term
# None labels are treated as abstains
if not label:
continue
# check span-specific rules
if self.span_rule and label == 1:
span = Span(char_start, char_end - 1, sentence)
if self.span_rule(span):
label = 2
flip = True
if term.lower() in self.stopwords or term in self.stopwords:
label = 2
#label = self.stopwords[key]
start, end = get_word_index_span((char_start, char_end - 1),
sentence)
for i in range(start, end + 1):
L[i] = label
flip = False
return L