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 print_pre_change_info(out, auto, gold, auto_mentions, gold_mention_set, text, parses, heads, gold_clusters, gold_mentions, gold_doc, auto_clusters): # Cataphora mentions = defaultdict(lambda: [None, None, None]) for cluster in gold: non_pronoun = min_non_pronoun(cluster, text, parses, heads) for mention in cluster: mtype = coreference.mention_type(mention, text, parses, heads) if mtype == 'pronoun': if non_pronoun is not None and mention < non_pronoun: mentions[mention][0] = True else: mentions[mention][0] = False for cluster in auto: non_pronoun = min_non_pronoun(cluster, text, parses, heads) for mention in cluster: mtype = coreference.mention_type(mention, text, parses, heads) if mtype == 'pronoun': if non_pronoun is not None and mention < non_pronoun: mentions[mention][1] = True else: mentions[mention][1] = False in_both = [] for mention in mentions: if mentions[mention][0] and mentions[mention][1]: in_both.append(mention) for mention in in_both: acluster = auto_clusters[auto_mentions[mention]] gcluster = gold_clusters[gold_mentions[mention]] anon_pronoun = min_non_pronoun(acluster, text, parses, heads) gnon_pronoun = min_non_pronoun(gcluster, text, parses, heads) if anon_pronoun == gnon_pronoun: mentions[mention][2] = True else: mentions[mention][2] = False for mention in mentions: mtext = coreference_rendering.mention_text(text, mention).lower() print >> out['out'], "Cataphoric properties", mentions[mention], mtext
def categorise(auto, gold, changes, text, parses, heads, gold_mention_set, auto_mentions, gold_doc):
# Not an Entity
# A set of splits to singles that cover an entire cluster
to_add = defaultdict(lambda: [])
for split in changes['split']:
is_disjoint = True
for mention in split[1]:
if mention in gold_mention_set:
mtype = coreference.mention_type(mention, text, parses, heads)
if mtype != 'pronoun':
is_disjoint = False
break
if is_disjoint:
all_extra = True
for mention in split[0]:
if mention in gold_mention_set:
all_extra = False
break
if all_extra:
to_add[tuple(split[1])].append(split)
for cluster in to_add:
splits = to_add[cluster]
cluster = set(cluster)
split_cluster = set()
for split in splits:
split_cluster.update(split[0])
if len(split_cluster) == 1:
continue
properties = ['extra'] + cluster_error_properties(split_cluster, text, parses, heads, gold_doc)
changes['extra entity'].append((split_cluster, cluster.copy(), properties))
for split in splits:
changes['split'].remove(split)
to_remove = None
for remove in changes['remove']:
if iter(split[0]).next() in remove[0]:
to_remove = remove
break
if to_remove is not None:
changes['remove'].remove(to_remove)
# Missed Entity
# A set of merges of singles that form a single cluster
to_remove = []
for cluster in gold:
is_disjoint = True
missing = 0
for mention in cluster:
if mention not in auto_mentions:
missing += 1
else:
if coreference.mention_type(mention, text, parses, heads) != 'pronoun':
is_disjoint = False
break
if is_disjoint and missing > 1:
properties = ['missing'] + cluster_error_properties(cluster, text, parses, heads, gold_doc)
changes['missing entity'].append((cluster.copy(),properties))
for mention in cluster:
if mention in auto_mentions:
continue
operations = []
for merge in changes['merge']:
if len(merge[0]) == 1 and mention in merge[0]:
operations.append(merge)
break
for introduce in changes['introduce']:
if len(introduce[0]) == 1 and mention in introduce[0]:
operations.append(introduce)
break
to_remove.append(tuple(operations))
for merge, introduce in to_remove:
changes['merge'].remove(merge)
changes['introduce'].remove(introduce)
# Remove the splits and merges that involve the earliest non-pronoun mentions in the cluster
to_remove = []
for split in changes['split']:
if min_non_pronoun(split[0], text, parses, heads) == min_non_pronoun(split[1], text, parses, heads):
if min_non_pronoun(split[0], text, parses, heads) is None and min(split[0]) != min(split[1]):
continue
found = False
for remove in changes['remove']:
if split[0] == remove[0]:
to_remove.append((split, remove))
found = True
break
if not found:
to_remove.append((split, None))
for split, remove in to_remove:
changes['split'].remove(split)
if remove is not None:
changes['remove'].remove(remove)
to_remove = []
for merge in changes['merge']:
if min_non_pronoun(merge[0], text, parses, heads) == min_non_pronoun(merge[1], text, parses, heads):
if min_non_pronoun(merge[0], text, parses, heads) is None and min(merge[0]) != min(merge[1]):
continue
found = False
for introduce in changes['introduce']:
if introduce[0] == merge[0]:
found = True
to_remove.append((merge, introduce))
break
if not found:
to_remove.append((merge, None))
for merge, introduce in to_remove:
changes['merge'].remove(merge)
if introduce is not None:
changes['introduce'].remove(introduce)
# Remaining cases of splitting a singleton, which does not get merged, are incorrectly referential
to_remove = []
for split in changes['split']:
if len(split[0]) == 1:
if split[2] != '':
to_remove.append(split)
for split in to_remove:
changes['split'].remove(split)
to_remove = None
for remove in changes['remove']:
if iter(split[0]).next() in remove[0]:
to_remove = remove
break
if to_remove is not None:
changes['remove'].remove(to_remove)
properties = ['extra'] + mention_error_properties(iter(split[0]).next(), split[1], text, parses, heads, gold_doc)
changes['extra mention'].append((split[0], split, properties))
# Pair up introduces and merges to form incorrectly non-referential
to_remove = []
for merge in changes['merge']:
if len(merge[0]) == 1:
elsewhere = False
for split in changes['split']:
if len(split[0]) == 1:
smention = list(split[0])[0]
mmention = list(merge[0])[0]
if smention == mmention:
elsewhere = True
break
if not elsewhere:
mention = list(merge[0])[0]
if mention != min_non_pronoun(merge[1], text, parses, heads) and mention not in auto_mentions:
properties = ['missing'] + mention_error_properties(mention, merge[1], text, parses, heads, gold_doc)
changes['missing mention'].append(({mention}, merge[1], merge, properties))
for introduce in changes['introduce']:
if len(introduce[0]) == 1 and mention in introduce[0]:
to_remove.append((merge, introduce))
break
for merge, introduce in to_remove:
changes['merge'].remove(merge)
changes['introduce'].remove(introduce)
return changes
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 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
