def capitalized(self):
"""Returns the first capitalized type for this class if it exists"""
for t in self.types:
if punctuation.is_capitalized(t):
return t
return self.normalized
def record(self, token,
left_context=EMPTY_TOKEN, right_context=EMPTY_TOKEN):
self.types.add(token.type)
self.count += 1
if punctuation.is_capitalized(token.type):
self.upper_count += 1
if token.type.startswith(u'.') == False and \
(right_context.type == u'.' or token.type.endswith(u'.')):
self.abbr_count += 1
def segment(text_or_tokens, raw=False, classifier=CLASSIFIER):
tokens = tokenize(text_or_tokens) if raw else text_or_tokens
n_tokens = len(tokens)
# Classify tokens for abbreviation and proper noun detection
if not classifier:
classifier = TokenClassifier()
classifier.train(tokens)
sentences, cache = [], []
closing = False
stack = SpanishPunctuationStack()
for i, t in enumerate(tokens):
# Always segment on a newline
if t.match(punctuation.NEWLINE_RE): # Newline
if cache:
if raw:
start, end = cache[0].start, cache[-1].end
sentences.append(text_or_tokens[start:end])
else:
sentences.append(cache)
cache = []
stack = SpanishPunctuationStack()
closing = False
continue
stack.feed(t.type)
cache.append(t)
# Classify local context
current_class = classifier.classify(t)
next_token = tokens[i + 1] if i < n_tokens - 1 else EMPTY_TOKEN
next_class = classifier.classify(next_token)
# Look for the next sentence segment marker
# The candidates are abbreviations and literal markers
if not closing:
if t.type.endswith(u'.') or current_class.is_abbreviation:
# Segment on this abbreviation
# if the the next token is capitalized and not a proper noun
if punctuation.is_capitalized(next_token.type) \
and not next_class.is_proper_noun:
closing = True
elif t.match(punctuation.SEGMENT_RE):
# Segment at this literal segment marker
# if punctuation.is_capitalized(next_token):
closing = True
# If we have found the sentence segment marker or reached the end,
# check for any remaining tokens then end the sentence
if closing or not next_token:
if not stack.pending() or not next_token:
if raw:
start, end = cache[0].start, cache[-1].end
sentences.append(text_or_tokens[start:end])
else:
sentences.append(cache)
cache = []
stack = SpanishPunctuationStack()
closing = False
return sentences
