def generate_y(self, min_char_match=20):
"""
returns X: list of sentence strings
y: numpy vector of 1, -1 (for positive/negative examples)
"""
good_match = False # this will be set to True if sufficent matching characters in
# at least one of the parts of the quotes
match_indices = []
# go through quotes, match using difflib
# and keep any matches which are long enough so likely true matches
for quote in self.quotes:
self.sequencematcher.set_seq1(quote)
best_match = self.sequencematcher.find_longest_match(0, len(quote), 0, self.lenpdf)
# only interested in good quality matches
if best_match.size > min_char_match:
good_match = True
match_indices.append((best_match.b, best_match.b + best_match.size)) # add (start_i, end_i) tuples (of PDF indices)
y = []
if not good_match:
# if quality criteria not met, leave here
# (i.e. return empty lists [], [])
return y
# otherwise continue and generate feature and answer vectors
# get indices of sentences (rather than split)
sent_indices = sent_tokenizer.span_tokenize(self.pdftext)
# go through sentence indices
# make X (list of sentences)
# and calculate y, if there is *any* overlap with matched quoted text then
# y = True
for (start_i, end_i) in sent_indices:
# if any overlaps with quotes, then y = True, else False
if any((self._overlap((start_i, end_i), match_tuple) for match_tuple in match_indices)):
y.append(1)
else:
y.append(-1)
return y
def load_pdftext(self, pdftext):
self.pdftext = pdftext
self.lenpdf = len(pdftext)
self.sequencematcher.set_seq2(self.pdftext)
self.sent_indices = sent_tokenizer.span_tokenize(self.pdftext)
