def min_non_pronoun(cluster, text, parses, heads, check_head=False): ans = None for mention in cluster: if coreference.mention_type(mention, text, parses, heads) == 'pronoun': continue if check_head: head = coreference.mention_head(mention, text, parses, heads, default_last=True) if coreference.mention_type((mention[0], head[0][0], head[0][1]), text, parses, heads) == 'pronoun': continue if ans is None or ans > mention: ans = mention return ans
def cluster_error_properties(cluster, text, parses, heads, gold_doc): ans = [] # How big is the cluster ans.append(len(cluster)) # Counts of each type in the cluster counts = [0, 0, 0] for mention in cluster: mtype = coreference.mention_type(mention, text, parses, heads) if mtype == 'name': counts[0] += 1 elif mtype == 'nominal': counts[1] += 1 elif mtype == 'pronoun': counts[2] += 1 ans += counts # If it is one pronoun and something else, more info on the pronoun if counts[0] + counts[1] == 1 and counts[2] == 1: pronoun = None for mention in cluster: mtype = coreference.mention_type(mention, text, parses, heads) if mtype == 'pronoun': pronoun = mention mtext = coreference_rendering.mention_text(text, pronoun).lower() ans.append(mtext) else: ans.append(None) # Number of cataphoric pronouns cataphora = 0 non_pronoun = min_non_pronoun(cluster, text, parses, heads, True) for mention in cluster: if mention < non_pronoun: mtype = coreference.mention_type(mention, text, parses, heads) if mtype == 'pronoun': cataphora += 1 ans.append(cataphora) # NER types ner = set() for mention in cluster: if mention in gold_doc['ner']: ner.add(gold_doc['ner'][mention]) ner = list(ner) ner.sort() ans.append(ner) # Are all the mentions the same? mtext = set() for mention in cluster: mtext.add(coreference_rendering.mention_text(text, mention).lower()) ans.append(len(mtext) == 1) # Are all the heads the same? mhead = set() for mention in cluster: mhead.add(coreference.mention_head(mention, text, parses, heads)[1].lower()) ans.append(len(mhead) == 1) return ans
def mention_error_properties(mention, cluster, text, parses, heads, gold_doc):
ans = []
rest = cluster.difference({mention})
# Type of mention
mtype = coreference.mention_type(mention, text, parses, heads)
ans.append(mtype)
# Text of mention
mtext = coreference_rendering.mention_text(text, mention).lower()
ans.append('_'.join(mtext.split()))
# Does it have a string match with something in the cluster?
matches = 'no_text_match'
for omention in rest:
otext = coreference_rendering.mention_text(text, omention).lower()
if otext == mtext:
matches = 'text_match'
break
ans.append(matches)
# Does it have a head match with something in the cluster?
matches = 'no_head_match'
mhead = coreference.mention_head(mention, text, parses, heads)[1].lower()
for omention in rest:
ohead = coreference.mention_head(omention, text, parses, heads)[1].lower()
if mhead == ohead:
matches = 'head_match'
break
ans.append(matches)
# Is it nested within another mention in the cluster
nested = 'not_nested'
for omention in rest:
if omention[0] == mention[0]:
if mention[1] < omention[1] and omention[2] < mention[2]:
if nested == 'nested_inside':
nested = 'nested_both'
break
else:
nested = 'nested_outside'
if omention[1] < mention[1] and mention[2] < omention[2]:
if nested == 'nested_outside':
nested = 'nested_both'
break
else:
nested = 'nested_inside'
ans.append(nested)
# Was it first in the cluster?
ans.append(mention == min(cluster))
# Was it last in the cluster?
ans.append(mention == max(cluster))
# Is it a case of cataphora?
non_pronoun = min_non_pronoun(cluster, text, parses, heads)
ans.append(non_pronoun is not None and mention < non_pronoun)
# Do NER, number, person, or gender of mention and cluster match?
cluster_properties = get_cluster_info(rest, gold_doc)
mention_properties = get_cluster_info({mention}, gold_doc)
words = ['ner', 'number', 'person', 'gender']
for i in xrange(4):
if len(mention_properties[i]) == 0 or len(cluster_properties[i]) == 0:
ans.append(words[i] + '_unknown')
elif len(mention_properties[i].intersection(cluster_properties[i])) > 0:
ans.append(words[i] + '_matches')
else:
ans.append(words[i] + '_does_not_match')
return ans
def match_boundaries(gold_mention_set, auto_mention_set, auto_mentions, auto_clusters, text, parses, heads):
changed = set()
# Apply changes for cases where the difference is only leading or trailing punctuation
mapping = {}
used_gold = set()
unique_to_gold = gold_mention_set.difference(auto_mention_set)
unique_to_auto = auto_mention_set.difference(gold_mention_set)
for amention in unique_to_auto:
sentence, astart, aend = amention
while (astart < aend - 1 and
(text[sentence][astart] == "the" or
(len(text[sentence][astart]) == 1 and
text[sentence][astart][0] not in string.letters))):
astart += 1
while (astart < aend - 1 and
(text[sentence][aend - 1] == "'s" or
(len(text[sentence][aend - 1]) == 1 and
text[sentence][aend - 1][0] not in string.letters))):
aend -= 1
for gmention in unique_to_gold:
if gmention in used_gold:
continue
gsentence, gstart, gend = gmention
if sentence != gsentence:
continue
while (gstart < gend - 1 and
(text[sentence][gstart] == "the" or
(len(text[sentence][gstart]) == 1 and
text[sentence][gstart][0] not in string.letters))):
gstart += 1
while (gstart < gend - 1 and
(text[sentence][gend - 1] == "'s" or
(len(text[sentence][gend - 1]) == 1 and
text[sentence][gend - 1][0] not in string.letters))):
gend -= 1
if astart == gstart and aend == gend:
mapping[amention] = gmention
used_gold.add(gmention)
# Apply mapping to create new auto_mention_set
for mention in mapping:
auto_mention_set.remove(mention)
auto_mention_set.add(mapping[mention])
cluster_id = auto_mentions.pop(mention)
auto_mentions[mapping[mention]] = cluster_id
auto_clusters[cluster_id].remove(mention)
auto_clusters[cluster_id].append(mapping[mention])
changed.add((mention, mapping[mention]))
# Create a mapping based on heads
head_dict = defaultdict(lambda: {'auto': [], 'gold': []})
for mention in auto_mention_set.difference(gold_mention_set):
sentence, start, end = mention
head = coreference.mention_head(mention, text, parses, heads, default_last=True)
# This will default to last word if the mention is not a constituent, is
# there an alternative?
if head is not None:
head = (mention[0], head[0])
head_dict[head]['auto'].append(mention)
for mention in gold_mention_set.difference(auto_mention_set):
sentence, start, end = mention
head = coreference.mention_head(mention, text, parses, heads, default_last=True)
if head is not None:
head = (mention[0], head[0])
head_dict[head]['gold'].append(mention)
mapping = {}
for head in head_dict:
amentions = head_dict[head]['auto']
gmentions = head_dict[head]['gold']
if len(amentions) == 1 and len(gmentions) == 1:
mapping[amentions[0]] = gmentions[0]
# Apply mapping to create new auto_mention_set
for mention in mapping:
auto_mention_set.remove(mention)
auto_mention_set.add(mapping[mention])
cluster_id = auto_mentions.pop(mention)
auto_mentions[mapping[mention]] = cluster_id
auto_clusters[cluster_id].remove(mention)
auto_clusters[cluster_id].append(mapping[mention])
changed.add((mention, mapping[mention]))
# Add notes
nchanges = []
for smention, gmention in changed:
properties = [smention, gmention]
pre_extra_text = None
pre_missing_text = None
post_extra_text = None
post_missing_text = None
pre_extra_nodes = None
pre_missing_nodes = None
post_extra_nodes = None
post_missing_nodes = None
if smention[1] < gmention[1]:
pre_extra_text = ' '.join(text[smention[0]][smention[1]:gmention[1]]).lower()
nodes = parses[gmention[0]].get_spanning_nodes(smention[1], gmention[1])
pre_extra_nodes = ' '.join([node.label for node in nodes])
if smention[1] > gmention[1]:
pre_missing_text = ' '.join(text[smention[0]][gmention[1]:smention[1]]).lower()
nodes = parses[gmention[0]].get_spanning_nodes(gmention[1], smention[1])
pre_missing_nodes = ' '.join([node.label for node in nodes])
if smention[2] < gmention[2]:
post_missing_text = ' '.join(text[smention[0]][smention[2]:gmention[2]]).lower()
nodes = parses[gmention[0]].get_spanning_nodes(smention[2], gmention[2])
post_missing_nodes = ' '.join([node.label for node in nodes])
if smention[2] > gmention[2]:
post_extra_text = ' '.join(text[smention[0]][gmention[2]:smention[2]]).lower()
nodes = parses[gmention[0]].get_spanning_nodes(gmention[2], smention[2])
post_extra_nodes = ' '.join([node.label for node in nodes])
snode = parses[smention[0]].get_nodes('lowest', smention[1], smention[2])
properties.append("in the parse" if snode is not None else "not in the parse")
properties.append(pre_extra_text)
properties.append(pre_missing_text)
properties.append(post_extra_text)
properties.append(post_missing_text)
properties.append(pre_extra_nodes)
properties.append(pre_missing_nodes)
properties.append(post_extra_nodes)
properties.append(post_missing_nodes)
nchanges.append(tuple(properties))
return nchanges
def split_merge_properties(part, cluster, auto, gold, text, parses, heads, gold_mentions, gold_clusters, auto_mentions, gold_doc):
ans = []
rest = cluster.difference(part)
# Size of part
ans.append(len(part)) # 0
# Size of rest
ans.append(len(rest)) # 1
# If size 1, what the text is
mtext = None
if len(part) == 1:
mention = iter(part).next()
mtext = '_'.join(coreference_rendering.mention_text(text, mention).lower().split())
ans.append(mtext) # 2
# Does this part have any cataphoric pronouns
count = 0
acluster = set()
for mention in cluster:
if mention in auto_mentions:
acluster.add(mention)
non_pronoun = min_non_pronoun(acluster, text, parses, heads)
if non_pronoun is not None and non_pronoun not in part:
for mention in part:
if mention in auto_mentions and mention < non_pronoun:
mtype = coreference.mention_type(mention, text, parses, heads)
if mtype == 'pronoun':
count += 1
ans.append("%d_cataphoric" % count)
# Number of pronouns, nominals, names present in it
type_counts = {'pronoun': 0, 'name': 0, 'nominal': 0}
for mention in part:
mtype = coreference.mention_type(mention, text, parses, heads)
type_counts[mtype] += 1
ans.append(type_counts['name']) # 3
ans.append(type_counts['nominal']) # 4
ans.append(type_counts['pronoun']) # 5
# Number of pronouns, nominals, names, in rest
type_counts = {'pronoun': 0, 'name': 0, 'nominal': 0}
for mention in rest:
mtype = coreference.mention_type(mention, text, parses, heads)
type_counts[mtype] += 1
ans.append(type_counts['name']) # 6
ans.append(type_counts['nominal']) # 7
ans.append(type_counts['pronoun']) # 8
# Whether this is extra
all_extra = True
for mention in part:
if mention in gold_mentions:
all_extra = False
ans.append(all_extra) # 9
# Whether the rest is all extra
all_extra = True
for mention in rest:
if mention in gold_mentions:
all_extra = False
ans.append(all_extra) # 10
# Whether there is an exact string match between a mention in the part and cluster (excluding pronouns)
match_present = 'no_string_match'
for smention in part:
mtype = coreference.mention_type(smention, text, parses, heads)
if mtype == 'pronoun':
continue
for rmention in rest:
mtype = coreference.mention_type(rmention, text, parses, heads)
if mtype == 'pronoun':
continue
stext = coreference_rendering.mention_text(text, smention).lower()
rtext = coreference_rendering.mention_text(text, rmention).lower()
if stext == rtext:
match_present = 'string_match'
break
if 'no' not in match_present:
break
ans.append(match_present) # 11
# Whether there is a head match between a mention in the part and cluster (excluding pronouns)
match_present = 'no_head_match'
for smention in part:
mtype = coreference.mention_type(smention, text, parses, heads)
if mtype == 'pronoun':
continue
for rmention in rest:
mtype = coreference.mention_type(rmention, text, parses, heads)
if mtype == 'pronoun':
continue
shead = coreference.mention_head(smention, text, parses, heads)[1].lower()
rhead = coreference.mention_head(rmention, text, parses, heads)[1].lower()
if shead == rhead:
match_present = 'head_match'
break
if 'no' not in match_present:
break
ans.append(match_present) # 12
# What has happened, or will happen
example = iter(part).next()
action = 'nothing'
if example not in gold_mentions:
action = 'delete'
elif part != set(gold_clusters[gold_mentions[example]]):
action = 'merge'
ans.append(action) # 13
action = 'nothing'
if example not in auto_mentions:
action = 'introduce'
else:
for acluster in auto:
if example in acluster:
if acluster != part:
action = 'split'
break
ans.append(action) # 14
# NER, number, person, gender
cproperties = get_cluster_info(rest, gold_doc)
pproperties = get_cluster_info(part, gold_doc)
for prop in xrange(4):
ans.append(cproperties[prop] == pproperties[prop])
cprop = list(cproperties[prop])
cprop.sort()
pprop = list(pproperties[prop])
pprop.sort()
ans.append('part_' + '_'.join(pprop))
ans.append('cluster_' + '_'.join(cprop))
return ans
def match_boundaries(gold_mention_set, auto_mention_set, auto_mentions,
auto_clusters, auto_cluster_set, text, parses, heads):
# Apply changes for cases where the difference is only leading or trailing
# punctuation
mapping = {}
used_gold = set()
unique_to_gold = gold_mention_set.difference(auto_mention_set)
unique_to_auto = auto_mention_set.difference(gold_mention_set)
for amention in unique_to_auto:
sentence, astart, aend = amention
while (aend - astart > 1
and (text[sentence][astart] == "the"
or (len(text[sentence][astart]) == 1
and text[sentence][astart][0]
not in string.ascii_letters))):
astart += 1
while (aend - astart > 1
and (text[sentence][aend - 1] == "'s"
or (len(text[sentence][aend - 1]) == 1
and text[sentence][aend - 1][0]
not in string.ascii_letters))):
aend -= 1
for gmention in unique_to_gold:
gsentence, gstart, gend = gmention
if sentence != gsentence or gmention in used_gold:
continue
while (gend - gstart > 1
and (text[sentence][gstart] == "the"
or (len(text[sentence][gstart]) == 1
and text[sentence][gstart][0]
not in string.ascii_letters))):
gstart += 1
while (gend - gstart > 1
and (text[sentence][gend - 1] == "'s"
or (len(text[sentence][gend - 1]) == 1
and text[sentence][gend - 1][0]
not in string.ascii_letters))):
gend -= 1
if astart == gstart and aend == gend:
mapping[amention] = gmention
used_gold.add(gmention)
# Apply mapping to create new auto_mention_set
for mention in mapping:
auto_mention_set.remove(mention)
auto_mention_set.add(mapping[mention])
cluster_id = auto_mentions.pop(mention)
auto_mentions[mapping[mention]] = cluster_id
auto_clusters[cluster_id].remove(mention)
auto_clusters[cluster_id].append(mapping[mention])
to_remove = None
for cluster in auto_cluster_set:
if mention in cluster:
to_remove = cluster
auto_cluster_set.remove(to_remove)
ncluster = []
for mention2 in to_remove:
if mention2 == mention:
mention2 = mapping[mention]
ncluster.append(mention2)
ncluster = tuple(ncluster)
auto_cluster_set.add(ncluster)
# Create a mapping based on heads
head_dict = defaultdict(lambda: {'auto': [], 'gold': []})
for mention in auto_mention_set.difference(gold_mention_set):
sentence, start, end = mention
head = coreference.mention_head(mention,
text,
parses,
heads,
default_last=True)
# This will default to last word if the mention is not a constituent,
# is there an alternative?
if head is not None:
head = (mention[0], head[0])
head_dict[head]['auto'].append(mention)
for mention in gold_mention_set.difference(auto_mention_set):
sentence, start, end = mention
head = coreference.mention_head(mention,
text,
parses,
heads,
default_last=True)
if head is not None:
head = (mention[0], head[0])
head_dict[head]['gold'].append(mention)
mapping = {}
for head in head_dict:
amentions = head_dict[head]['auto']
gmentions = head_dict[head]['gold']
if len(amentions) == 1 and len(gmentions) == 1:
mapping[amentions[0]] = gmentions[0]
# Apply mapping to create new auto_mention_set
for mention in mapping:
auto_mention_set.remove(mention)
auto_mention_set.add(mapping[mention])
cluster_id = auto_mentions.pop(mention)
auto_mentions[mapping[mention]] = cluster_id
auto_clusters[cluster_id].remove(mention)
auto_clusters[cluster_id].append(mapping[mention])
to_remove = None
for cluster in auto_cluster_set:
if mention in cluster:
to_remove = cluster
auto_cluster_set.remove(to_remove)
ncluster = []
for mention2 in to_remove:
if mention2 == mention:
mention2 = mapping[mention]
ncluster.append(mention2)
ncluster = tuple(ncluster)
auto_cluster_set.add(ncluster)
