def is_generic_you(attr, ment, sents):
if not (ment[2] - ment[1] == 1 and attr['head_word'] == 'you'):
return False
if ment[2] + 1 <= len(sents[ment[0]]):
next_word = coref.mention_text((ment[0], ment[2], ment[2] + 1), sents)
if next_word.lower() == 'know':
return True
if ment[1] - 1 >= 0:
prev_word = coref.mention_text((ment[0], ment[1] - 1, ment[1]), sents)
if prev_word.lower() == 'thank':
return True
return False
def is_generic_you(attr, ment, sents):
if not (ment[2] - ment[1] == 1 and attr["head_word"] == "you"):
return False
if ment[2] + 1 <= len(sents[ment[0]]):
next_word = coref.mention_text((ment[0], ment[2], ment[2] + 1), sents)
if next_word.lower() == "know":
return True
if ment[1] - 1 >= 0:
prev_word = coref.mention_text((ment[0], ment[1] - 1, ment[1]), sents)
if prev_word.lower() == "thank":
return True
return False
def has_following_apostrophe(ment, sents):
if ment[2] + 1 <= len(sents[ment[0]]):
next_word = coref.mention_text((ment[0], ment[2], ment[2] + 1), sents)
if next_word == "'s":
return True
return False
def remove_conll_spurious_mentions(ments, sents, trees, heads, sner, params):
ments_to_remove = []
for ment in ments.keys():
if is_generic(ment, sents, trees, heads):
ments_to_remove.append(ment)
surface = coref.mention_text(ment, sents).lower()
if (ment in sner[ment[0]] and sner[ment[0]][ment] == 'GPE'
and surface in dictionaries.gpe_acronyms):
ments_to_remove.append(ment)
if surface in dictionaries.stop_words:
ments_to_remove.append(ment)
if start_with_stop_prefixes(surface):
ments_to_remove.append(ment)
if end_with_stop_suffixes(surface):
ments_to_remove.append(ment)
head_span, head_word, head_pos = \
coref.mention_head(ment, sents, trees, heads)
tmp = (ment[0], head_span[0], head_span[1])
if (tmp in sner[ment[0]]
and sner[ment[0]][tmp] in {'PERCENT', 'MONEY'}):
ments_to_remove.append(ment)
for r in ments_to_remove:
if r in ments:
ments.pop(r)
def is_pleonastic(attr, ment, sents, gold_ments=None):
if attr["type"] != my_constant.MAP_MTYPES["pronoun"]:
return False
if attr["surface"] == "it":
if ment[2] + 1 <= len(sents[ment[0]]):
next_word = coref.mention_text((ment[0], ment[2], ment[2] + 1), sents)
if next_word.lower() in dictionaries.pleonastic_words:
return True
if attr["surface"] == "you":
if ment[2] + 1 <= len(sents[ment[0]]):
next_word = coref.mention_text((ment[0], ment[2], ment[2] + 1), sents)
if next_word.lower() == "know":
return True
if ment[1] - 1 >= 0:
prev_word = coref.mention_text((ment[0], ment[1] - 1, ment[1]), sents)
if prev_word.lower() == "thank":
return True
return False
def is_pleonastic(attr, ment, sents, gold_ments=None):
if attr['type'] != my_constant.MAP_MTYPES['pronoun']:
return False
if attr['surface'] == 'it':
if ment[2] + 1 <= len(sents[ment[0]]):
next_word = coref.mention_text((ment[0], ment[2], ment[2] + 1),
sents)
if next_word.lower() in dictionaries.pleonastic_words:
return True
if attr['surface'] == 'you':
if ment[2] + 1 <= len(sents[ment[0]]):
next_word = coref.mention_text((ment[0], ment[2], ment[2] + 1),
sents)
if next_word.lower() == 'know':
return True
if ment[1] - 1 >= 0:
prev_word = coref.mention_text((ment[0], ment[1] - 1, ment[1]),
sents)
if prev_word.lower() == 'thank':
return True
return False
def print_missing_gold_mentions(data):
out = open('missing_gold_mentions.log', 'w')
order = []
for doc in data:
for part in data[doc]:
order.append((doc, part))
order.sort()
for doc, part in order:
sents = data[doc][part]['text']
trees = data[doc][part]['parses']
names = data[doc][part]['ner']
gold_mentions = data[doc][part]['mentions']
doc_mentions = data[doc][part]['doc_mentions']
print >> out, "# %s %s\n" % (doc, part)
num_missing = 0
num_not_con = 0
pred = {}
for ments in doc_mentions:
for m in ments:
pred[m] = True
gold = [g for g in gold_mentions]
gold.sort()
for g in gold:
if g not in pred:
num_missing += 1
node = trees[g[0]].get_nodes('lowest', g[1], g[2])
con = 1
if node is None:
num_not_con += 1
con = 0
ner = 1
if g not in names:
ner = 0
print >> out, "%s\t%d\t%d\t%s" % (g, con, ner,
coref.mention_text(g, sents))
if num_missing > 0:
print >> out, "\n#missing mentions = %d" % num_missing,
print >> out, "(%d are not constituents)\n" % num_not_con
return True
def print_missing_gold_mentions(data):
out = open('missing_gold_mentions.log', 'w')
order = []
for doc in data:
for part in data[doc]:
order.append((doc, part))
order.sort()
for doc, part in order:
sents = data[doc][part]['text']
trees = data[doc][part]['parses']
names = data[doc][part]['ner']
gold_mentions = data[doc][part]['mentions']
doc_mentions = data[doc][part]['doc_mentions']
print >> out, "# %s %s\n" % (doc, part)
num_missing = 0
num_not_con = 0
pred = {}
for ments in doc_mentions:
for m in ments:
pred[m] = True
gold = [g for g in gold_mentions]
gold.sort()
for g in gold:
if g not in pred:
num_missing += 1
node = trees[g[0]].get_nodes('lowest', g[1], g[2])
con = 1
if node is None:
num_not_con += 1
con = 0
ner = 1
if g not in names:
ner = 0
print >> out, "%s\t%d\t%d\t%s" % (g, con, ner,
coref.mention_text(g, sents))
if num_missing > 0:
print >> out, "\n#missing mentions = %d" % num_missing,
print >> out, "(%d are not constituents)\n" % num_not_con
return True
def init(doc_ments, sents, trees, heads, sner, speakers):
doc_attrs = {}
for sent_ments in doc_ments:
for ment in sent_ments:
attr = {}
attr["type"] = my_constant.MAP_MTYPES[coref.mention_type(ment, sents, trees, heads)]
attr["surface"] = coref.mention_text(ment, sents).lower()
set_head(attr, ment, sents, trees, heads)
set_first_word(attr, ment, sents, trees, heads)
set_ner(attr, ment, sner)
attr["relaxed_surface"] = remove_phrase_after_head(attr, ment, sents, trees, heads)
attr["word_list"] = extract_word_list(attr)
attr["modifiers"] = extract_modifiers(attr, ment, sents, trees, heads)
extract_properties(attr, ment, sents)
set_speaker(attr, ment, speakers)
attr["pleonastic"] = is_pleonastic(attr, ment, sents)
doc_attrs[ment] = attr
return doc_attrs
def init(doc_ments, sents, trees, heads, sner, speakers):
doc_attrs = {}
for sent_ments in doc_ments:
for ment in sent_ments:
attr = {}
attr['type'] = my_constant.MAP_MTYPES[coref.mention_type(
ment, sents, trees, heads)]
attr['surface'] = coref.mention_text(ment, sents).lower()
set_head(attr, ment, sents, trees, heads)
set_first_word(attr, ment, sents, trees, heads)
set_ner(attr, ment, sner)
attr['relaxed_surface'] = remove_phrase_after_head(
attr, ment, sents, trees, heads)
attr['word_list'] = extract_word_list(attr)
attr['modifiers'] = extract_modifiers(attr, ment, sents, trees,
heads)
extract_properties(attr, ment, sents)
set_speaker(attr, ment, speakers)
attr['pleonastic'] = is_pleonastic(attr, ment, sents)
doc_attrs[ment] = attr
return doc_attrs
