def get_local_features(self, text):
feature_vector = dict()
text_obj = TextObj(text)
feature_extractor.get_ngrams(feature_vector, text_obj.tokens)
feature_extractor.get_ngrams(feature_vector, text_obj.tokens, n=2)
feature_extractor.get_initialisms(feature_vector, text_obj.tokens)
feature_extractor.get_basic_lengths(feature_vector, text_obj.text, text_obj.sentences, text_obj.tokens)
feature_extractor.get_repeated_punct(feature_vector, text_obj.text)
feature_extractor.get_LIWC(feature_vector, text_obj.text)
return feature_vector
def build_features(self, feats=None, start=None, end=None): # default: None -> use default features
minIndex = self.occurrences[0].start
maxIndex = self.occurrences[-1].end
if start is None:
start = minIndex
if end is None:
end = maxIndex
assert minIndex <= start <= maxIndex, "Start index is beyond bounds."
assert minIndex <= end <= maxIndex, "End index is beyond bounds."
assert start <= end, "Start index is greater than end index."
startInd = self.getIndex(self.occurrences, lambda x,y: x.start>=y, start)
endInd = self.getIndex(self.occurrences, lambda x,y: x.end>y, end)
if endInd is None:
endInd = len(self.occurrences)-1
occurrences = self.occurrences[startInd:endInd]
text = self.text[start:end]
tokens = [o.text for o in occurrences]
feature_dependencies_in = [d for d in self.dependencies if startInd <= d.gov_index < endInd and startInd <= d.dep_index < endInd]
feature_dependencies_boundary = [d for d in self.dependencies if startInd <= d.gov_index < endInd != startInd <= d.dep_index < endInd]
# MPQA &c
if feats is None:
self.features = dict() # start fresh
# default features
feats = ['unigram', 'initialism', 'lengths', 'punctuation', 'quotes', 'liwc', 'dep']
for feat in feats:
if feat.endswith('gram'):
n = measure_to_int(feat)
feature_extractor.get_ngrams(self.features, tokens, n=n)
elif feat.endswith('alism'):
feature_extractor.get_initialisms(self.features, tokens, use_lowercase=True, finalism=(feat == 'finalism'))
elif feat.startswith('lengths'):
sentences = []
numSents = len(self.sentstarts)
for i in range(numSents):
if self.sentstarts[i] > end:
break
sStart = self.sentstarts[i]
sEnd = self.sentends[i]
if self.sentends[i] > start and self.sentstarts[i] < start:
sStart = start
elif self.sentends[i] > end and self.sentstarts[i] < end:
sEnd = end
sentences.append(self.text[sStart:sEnd])
words = tokens
feature_extractor.get_basic_lengths(self.features, text, sentences, words)
elif feat.startswith('punct'):
feature_extractor.get_repeated_punct(self.features, text)
elif feat.startswith('quot'):
feature_extractor.get_quoted_terms(self.features, text)
elif feat.lower() == 'liwc':
text_scores = Counter()
text_scores['Word Count'] = len(occurrences)
for o in occurrences:
text_scores.update(o.liwc)
text_scores = word_category_counter.normalize(text_scores)
for category, score in text_scores.items():
self.features['LIWC:'+category] = score
elif feat.lower() == 'dep':
dep_scores = Counter()
#pdb.set_trace()
for d in feature_dependencies_in:
dep_string = "%s(%s,%s)" % (d.relation, self.occurrences[d.gov_index].lemma, self.occurrences[d.dep_index].lemma)
dep_scores[dep_string] += 1
for dep, score in dep_scores.items():
self.features['dep:'+ dep] = score
