def create_lexicalized_tree(self, mrg, heads):
"""
Creates a lexicalized syntax tree given a MRG-style parse and a Penn2Malt style heads file.
"""
t = LexicalizedTree.parse(mrg, leaf_pattern = '(?<=\\s)[^\)\(]+') # Vanessa's modification
t.lexicalize(heads, from_string = True)
return t
def get_parsed_trees_from_string(self, tree_strings):
# tree_strings separated by "\n"
parsed_trees = []
for line in tree_strings:
line = line.strip()
if line != '':
parsed_trees.append(LexicalizedTree.parse(line, leaf_pattern = '(?<=\\s)[^\)\(]+'))
return parsed_trees
def get_parsed_trees_from_string(tree_strings):
# tree_strings separated by "\n"
parsed_trees = []
for line in tree_strings:
line = line.strip()
#print line
if line != '':
parsed_trees.append(
LexicalizedTree.parse(line, leaf_pattern='(?<=\\s)[^\)\(]+'))
return parsed_trees
def process_single_sentence(self, doc, raw_text, end_of_para):
sentence = Sentence(len(doc.sentences), raw_text + ('<s>' if not end_of_para else '<P>'), doc)
parse_tree_str, deps_str = self.parse_single_sentence(raw_text)
parse = LexicalizedTree.parse(parse_tree_str, leaf_pattern = '(?<=\\s)[^\)\(]+')
sentence.set_unlexicalized_tree(parse)
for (token_id, te) in enumerate(parse.leaves()):
word = te
token = Token(word, token_id + 1, sentence)
sentence.add_token(token)
heads = self.get_heads(sentence, deps_str.split('\n'))
sentence.heads = heads
sentence.set_lexicalized_tree(prep_utils.create_lexicalized_tree(parse, heads))
doc.add_sentence(sentence)
def do_segment(self, text, input_edus = None, parsed_filename = None, heads_filename = None, deps_filename = None):
"""
Segments a text into elementary discourse units.
Assumes that the text is pre-processed such that end of sentences are marked with <s>, end of paragraphs with <p>
Returns a list containing :
- a list of lexicalized syntax trees for each sentence
- a list of couples (m, n) indicating a discourse unit between tokens at index m and n in the corresponding tree
- a list of unescaped edus (including paragraph boundaries indications
"""
segmented_text = self.split_by_sentence(text)
if parsed_filename and heads_filename and deps_filename:
#print os.path.exists(parsed_filename)
unlexicalized_trees = self.get_parsed_trees_from_string(open(parsed_filename).readlines())
heads = open(heads_filename).read().split("\r\n\r\n")[:-1]
#print open(heads_filename).read()
# for head in heads:
# print head
lexicalized_trees = []
for line in open(parsed_filename).readlines():
line = line.strip()
if line != '':
t = LexicalizedTree.parse(line, leaf_pattern = '(?<=\\s)[^\)\(]+')
t.lexicalize(heads[len(lexicalized_trees)], from_string = True)
lexicalized_trees.append(t)
dep_parses = self.get_deps(deps_filename)
else:
unlexicalized_trees, heads, dep_parses = self.syntax_parser.parse(map(lambda x: x[0], segmented_text))
lexicalized_trees = map(lambda x, y: self.create_lexicalized_tree(x, y), unlexicalized_trees, heads)
# for tree in unlexicalized_trees:
# print tree
#print unlexicalized_trees
#print heads
edus_intervals_pairs = []
edus = []
if input_edus:
edus, edus_intervals_pairs = utils.utils.align_edus_with_syntax_trees(input_edus, lexicalized_trees, segmented_text, self.penn_special_chars)
else:
for i in range(0, len(lexicalized_trees)):
t = lexicalized_trees[i]
t_words = map(lambda x: t.unescape(x), t.leaves())
# Vanessa's modification
# check if breaks contains all exclude words
exclude_words = ".`':;!?"
eval_boundaries = self.segment_tree(t)
for j in range(len(eval_boundaries) - 1):
if eval_boundaries[j] == 1.0 and eval_boundaries[j + 1] == 1.0:
#if not t_words[j].strip(exclude_words) and not t_words[j + 1].strip(exclude_words):
if not t_words[j + 1].strip(exclude_words):
eval_boundaries[j] = -1.0
cur_edus_intervals = [k+1 for k in range(0, len(eval_boundaries)) if eval_boundaries[k] == +1.0]
if not 0 in cur_edus_intervals:
cur_edus_intervals = [0] + cur_edus_intervals
if not len(eval_boundaries) in cur_edus_intervals:
cur_edus_intervals.append(len(eval_boundaries))
#print cur_edus_intervals
cur_edus = []
cur_edus_intervals_pairs = []
for j in range(0, len(cur_edus_intervals) - 1):
cur_edus.append(t_words[cur_edus_intervals[j]:cur_edus_intervals[j+1]])
cur_edus_intervals_pairs.append((cur_edus_intervals[j], cur_edus_intervals[j+1]))
# Add eventual paragraph boundary
#if segmented_text[i][1] == True:
#cur_edus[len(cur_edus) - 1].append("<p>")
#print segmented_text[i][1]
cur_edus[len(cur_edus) - 1].append(segmented_text[i][1])
#print 'cur_edus', cur_edus
edus_intervals_pairs.append(cur_edus_intervals_pairs)
edus.extend(cur_edus)
#print lexicalized_trees
#print "edus_intervals_pairs: ", edus_intervals_pairs
#print "edus: ", edus
#print
return [lexicalized_trees, dep_parses, edus_intervals_pairs, edus]
