diff --git a/conf.py b/conf.py
index 648e472..b4c42e8 100644
--- a/conf.py
+++ b/conf.py
@@ -30,7 +30,6 @@ W2V_MODEL_PATH = os.path.join(MAIN_PATH, 'models', W2V_MODEL_NAME)
W2V_MODEL = Word2Vec.load(W2V_MODEL_PATH)
NEURAL_MODEL_PATH = os.path.join(MAIN_PATH, 'models', NEURAL_MODEL_NAME)
-NEURAL_MODEL = utils.initialize_neural_model(NEURAL_MODEL_ARCHITECTURE, NUMBER_OF_FEATURES, NEURAL_MODEL_PATH)
FREQ_LIST_PATH = os.path.join(MAIN_PATH, 'freq', FREQ_LIST_NAME)
FREQ_LIST = utils.load_freq_list(FREQ_LIST_PATH)
diff --git a/corneferencer/entities.py b/corneferencer/entities.py
index c1e1509..cc52b52 100644
--- a/corneferencer/entities.py
+++ b/corneferencer/entities.py
@@ -1,4 +1,4 @@
-from corneferencer.resolvers.vectors import get_mention_features
+from corneferencer.resolvers import vectors
class Text:
@@ -19,6 +19,9 @@ class Text:
return mnt
return None
+ def get_mentions(self):
+ return self.mentions
+
def get_sets(self):
sets = {}
for mnt in self.mentions:
@@ -62,4 +65,4 @@ class Mention:
self.sentence_id = sentence_id
self.first_in_sentence = first_in_sentence
self.first_in_paragraph = first_in_paragraph
- self.features = get_mention_features(self)
+ self.features = vectors.get_mention_features(self)
diff --git a/corneferencer/inout/mmax.py b/corneferencer/inout/mmax.py
index e30f551..62feb49 100644
--- a/corneferencer/inout/mmax.py
+++ b/corneferencer/inout/mmax.py
@@ -3,11 +3,11 @@ import shutil
from lxml import etree
-from conf import CLEAR_INPUT, CONTEXT, FREQ_LIST
+import conf
from corneferencer.entities import Mention, Text
-def read(inpath):
+def read(inpath, clear_mentions=conf.CLEAR_INPUT):
textname = os.path.splitext(os.path.basename(inpath))[0]
textdir = os.path.dirname(inpath)
@@ -15,11 +15,11 @@ def read(inpath):
words_path = os.path.join(textdir, '%s_words.xml' % textname)
text = Text(textname)
- text.mentions = read_mentions(mentions_path, words_path)
+ text.mentions = read_mentions(mentions_path, words_path, clear_mentions)
return text
-def read_mentions(mentions_path, words_path):
+def read_mentions(mentions_path, words_path, clear_mentions=conf.CLEAR_INPUT):
mentions = []
mentions_tree = etree.parse(mentions_path)
markables = mentions_tree.xpath("//ns:markable",
@@ -43,7 +43,7 @@ def read_mentions(mentions_path, words_path):
head = get_head(head_orth, mention_words)
mention_group = ''
- if markable.attrib['mention_group'] != 'empty' and not CLEAR_INPUT:
+ if markable.attrib['mention_group'] != 'empty' and not clear_mentions:
mention_group = markable.attrib['mention_group']
mention = Mention(mnt_id=markable.attrib['id'],
text=span_to_text(span, words, 'orth'),
@@ -189,7 +189,7 @@ def get_prec_context(mention_start, words):
while context_start >= 0:
if not word_to_ignore(words[context_start]):
context.append(words[context_start])
- if len(context) == CONTEXT:
+ if len(context) == conf.CONTEXT:
break
context_start -= 1
context.reverse()
@@ -222,7 +222,7 @@ def get_follow_context(mention_end, words):
while context_end < len(words):
if not word_to_ignore(words[context_end]):
context.append(words[context_end])
- if len(context) == CONTEXT:
+ if len(context) == conf.CONTEXT:
break
context_end += 1
return context
@@ -349,9 +349,8 @@ def get_rarest_word(words):
rarest_word = words[0]
for i, word in enumerate(words):
word_freq = 0
- if word['base'] in FREQ_LIST:
- word_freq = FREQ_LIST[word['base']]
-
+ if word['base'] in conf.FREQ_LIST:
+ word_freq = conf.FREQ_LIST[word['base']]
if i == 0 or word_freq < min_freq:
min_freq = word_freq
rarest_word = word
diff --git a/corneferencer/inout/tei.py b/corneferencer/inout/tei.py
index 81d4afc..b166719 100644
--- a/corneferencer/inout/tei.py
+++ b/corneferencer/inout/tei.py
@@ -4,7 +4,7 @@ import shutil
from lxml import etree
-from conf import CLEAR_INPUT, CONTEXT, FREQ_LIST
+import conf
from corneferencer.entities import Mention, Text
from corneferencer.utils import eprint
@@ -18,7 +18,7 @@ NSMAP = {None: TEI_NS,
'xi': XI_NS}
-def read(inpath):
+def read(inpath, clear_mentions=conf.CLEAR_INPUT):
textname = os.path.basename(inpath)
text = Text(textname)
@@ -49,7 +49,7 @@ def read(inpath):
eprint("Error: missing mentions layer for text %s!" % textname)
return None
- if os.path.exists(ann_coreference) and not CLEAR_INPUT:
+ if os.path.exists(ann_coreference) and not clear_mentions:
add_coreference_layer(ann_coreference, text)
return text
@@ -215,6 +215,9 @@ def get_mention(mention, mnt_id, segments, segments_ids, paragraph_id, sentence_
semh_id = get_fval(f).split('#')[-1]
semh = segments[semh_id]
+ if len(mnt_segments) == 0:
+ mnt_segments.append(semh)
+
(sent_segments, prec_context, follow_context,
first_in_sentence, first_in_paragraph) = get_context(mnt_segments, segments, segments_ids)
@@ -272,7 +275,7 @@ def get_prec_context(mention_start, segments, segments_ids):
while context_start >= 0:
if not word_to_ignore(segments[segments_ids[context_start]]):
context.append(segments[segments_ids[context_start]])
- if len(context) == CONTEXT:
+ if len(context) == conf.CONTEXT:
break
context_start -= 1
context.reverse()
@@ -285,7 +288,7 @@ def get_follow_context(mention_end, segments, segments_ids):
while context_end < len(segments):
if not word_to_ignore(segments[segments_ids[context_end]]):
context.append(segments[segments_ids[context_end]])
- if len(context) == CONTEXT:
+ if len(context) == conf.CONTEXT:
break
context_end += 1
return context
@@ -341,8 +344,8 @@ def get_rarest_word(words):
rarest_word = words[0]
for i, word in enumerate(words):
word_freq = 0
- if word['base'] in FREQ_LIST:
- word_freq = FREQ_LIST[word['base']]
+ if word['base'] in conf.FREQ_LIST:
+ word_freq = conf.FREQ_LIST[word['base']]
if i == 0 or word_freq < min_freq:
min_freq = word_freq
diff --git a/corneferencer/main.py b/corneferencer/main.py
index f9aebc7..9f85c5d 100644
--- a/corneferencer/main.py
+++ b/corneferencer/main.py
@@ -4,9 +4,11 @@ import sys
from argparse import ArgumentParser
from natsort import natsorted
-sys.path.append(os.path.abspath(os.path.join('..')))
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
import conf
+import utils
from inout import mmax, tei
from inout.constants import INPUT_FORMATS
from resolvers import resolve
@@ -27,22 +29,25 @@ def main():
if conf.NEURAL_MODEL_ARCHITECTURE == 'siamese':
resolver = conf.NEURAL_MODEL_ARCHITECTURE
eprint("Warning: Using %s resolver because of selected neural model architecture!" %
- conf.NEURAL_MODEL_ARCHITECTURE)
- process_texts(args.input, args.output, args.format, resolver, args.threshold)
+ conf.NEURAL_MODEL_ARCHITECTURE)
+ process_texts(args.input, args.output, args.format, resolver, args.threshold, args.model)
def parse_arguments():
parser = ArgumentParser(description='Corneferencer: coreference resolver using neural nets.')
+ parser.add_argument('-f', '--format', type=str, action='store',
+ dest='format', default=INPUT_FORMATS[0],
+ help='input format; default: %s; possibilities: %s'
+ % (INPUT_FORMATS[0], ', '.join(INPUT_FORMATS)))
parser.add_argument('-i', '--input', type=str, action='store',
dest='input', default='',
help='input file or dir path')
+ parser.add_argument('-m', '--model', type=str, action='store',
+ dest='model', default='',
+ help='neural model path; default: %s' % conf.NEURAL_MODEL_PATH)
parser.add_argument('-o', '--output', type=str, action='store',
dest='output', default='',
help='output path; if not specified writes output to standard output')
- parser.add_argument('-f', '--format', type=str, action='store',
- dest='format', default=INPUT_FORMATS[0],
- help='input format; default: %s; possibilities: %s'
- % (INPUT_FORMATS[0], ', '.join(INPUT_FORMATS)))
parser.add_argument('-r', '--resolver', type=str, action='store',
dest='resolver', default=RESOLVERS[0],
help='resolve algorithm; default: %s; possibilities: %s'
@@ -55,16 +60,17 @@ def parse_arguments():
return args
-def process_texts(inpath, outpath, informat, resolver, threshold):
+def process_texts(inpath, outpath, informat, resolver, threshold, model_path):
+ model = utils.initialize_neural_model(conf.NEURAL_MODEL_ARCHITECTURE, conf.NUMBER_OF_FEATURES, model_path)
if os.path.isdir(inpath):
- process_directory(inpath, outpath, informat, resolver, threshold)
+ process_directory(inpath, outpath, informat, resolver, threshold, model)
elif os.path.isfile(inpath):
- process_text(inpath, outpath, informat, resolver, threshold)
+ process_text(inpath, outpath, informat, resolver, threshold, model)
else:
eprint("Error: Specified input does not exist!")
-def process_directory(inpath, outpath, informat, resolver, threshold):
+def process_directory(inpath, outpath, informat, resolver, threshold, model):
inpath = os.path.abspath(inpath)
outpath = os.path.abspath(outpath)
@@ -75,38 +81,38 @@ def process_directory(inpath, outpath, informat, resolver, threshold):
textname = os.path.splitext(os.path.basename(filename))[0]
textoutput = os.path.join(outpath, textname)
textinput = os.path.join(inpath, filename)
- process_text(textinput, textoutput, informat, resolver, threshold)
+ process_text(textinput, textoutput, informat, resolver, threshold, model)
-def process_text(inpath, outpath, informat, resolver, threshold):
+def process_text(inpath, outpath, informat, resolver, threshold, model):
basename = os.path.basename(inpath)
if informat == 'mmax' and basename.endswith('.mmax'):
print (basename)
text = mmax.read(inpath)
if resolver == 'incremental':
- resolve.incremental(text, threshold)
+ resolve.incremental(text, threshold, model)
elif resolver == 'entity_based':
- resolve.entity_based(text, threshold)
+ resolve.entity_based(text, threshold, model)
elif resolver == 'closest':
- resolve.closest(text, threshold)
+ resolve.closest(text, threshold, model)
elif resolver == 'siamese':
- resolve.siamese(text, threshold)
+ resolve.siamese(text, threshold, model)
elif resolver == 'all2all':
- resolve.all2all(text, threshold)
+ resolve.all2all(text, threshold, model)
mmax.write(inpath, outpath, text)
elif informat == 'tei':
print (basename)
text = tei.read(inpath)
if resolver == 'incremental':
- resolve.incremental(text, threshold)
+ resolve.incremental(text, threshold, model)
elif resolver == 'entity_based':
- resolve.entity_based(text, threshold)
+ resolve.entity_based(text, threshold, model)
elif resolver == 'closest':
- resolve.closest(text, threshold)
+ resolve.closest(text, threshold, model)
elif resolver == 'siamese':
- resolve.siamese(text, threshold)
+ resolve.siamese(text, threshold, model)
elif resolver == 'all2all':
- resolve.all2all(text, threshold)
+ resolve.all2all(text, threshold, model)
tei.write(inpath, outpath, text)
diff --git a/corneferencer/prepare_data.py b/corneferencer/prepare_data.py
new file mode 100644
index 0000000..6591929
--- /dev/null
+++ b/corneferencer/prepare_data.py
@@ -0,0 +1,140 @@
+# -*- coding: utf-8 -*-
+
+import codecs
+import os
+import random
+import sys
+
+from itertools import combinations
+from argparse import ArgumentParser
+from natsort import natsorted
+
+sys.path.append(os.path.abspath(os.path.join('..')))
+
+from inout import mmax, tei
+from inout.constants import INPUT_FORMATS
+from utils import eprint
+from corneferencer.resolvers import vectors
+
+
+POS_COUNT = 0
+NEG_COUNT = 0
+
+
+def main():
+ args = parse_arguments()
+ if not args.input:
+ eprint("Error: Input file(s) not specified!")
+ elif args.format not in INPUT_FORMATS:
+ eprint("Error: Unknown input file format!")
+ else:
+ process_texts(args.input, args.output, args.format, args.proportion)
+
+
+def parse_arguments():
+ parser = ArgumentParser(description='Corneferencer: data preparator for neural nets training.')
+ parser.add_argument('-i', '--input', type=str, action='store',
+ dest='input', default='',
+ help='input dir path')
+ parser.add_argument('-o', '--output', type=str, action='store',
+ dest='output', default='',
+ help='output path; if not specified writes output to standard output')
+ parser.add_argument('-f', '--format', type=str, action='store',
+ dest='format', default=INPUT_FORMATS[0],
+ help='input format; default: %s; possibilities: %s'
+ % (INPUT_FORMATS[0], ', '.join(INPUT_FORMATS)))
+ parser.add_argument('-p', '--proportion', type=int, action='store',
+ dest='proportion', default=5,
+ help='negative examples proportion; default: 5')
+ args = parser.parse_args()
+ return args
+
+
+def process_texts(inpath, outpath, informat, proportion):
+ if os.path.isdir(inpath):
+ process_directory(inpath, outpath, informat, proportion)
+ else:
+ eprint("Error: Specified input does not exist or is not a directory!")
+
+
+def process_directory(inpath, outpath, informat, proportion):
+ inpath = os.path.abspath(inpath)
+ outpath = os.path.abspath(outpath)
+
+ try:
+ create_data_vectors(inpath, outpath, informat, proportion)
+ finally:
+ print ('Positives: ', POS_COUNT)
+ print ('Negatives: ', NEG_COUNT)
+
+
+def create_data_vectors(inpath, outpath, informat, proportion):
+ features_file = codecs.open(outpath, 'w', 'utf-8')
+
+ files = os.listdir(inpath)
+ files = natsorted(files)
+
+ for filename in files:
+ textname = os.path.splitext(os.path.basename(filename))[0]
+ textinput = os.path.join(inpath, filename)
+
+ print ('Processing text: %s' % textname)
+ text = None
+ if informat == 'mmax' and filename.endswith('.mmax'):
+ text = mmax.read(textinput, False)
+ elif informat == 'tei':
+ text = tei.read(textinput, False)
+
+ positives, negatives = diff_mentions(text, proportion)
+ write_features(features_file, positives, negatives)
+
+
+def diff_mentions(text, proportion):
+ sets = text.get_sets()
+ all_mentions = text.get_mentions()
+ positives = get_positives(sets)
+ positives, negatives = get_negatives_and_update_positives(all_mentions, positives, proportion)
+ return positives, negatives
+
+
+def get_positives(sets):
+ positives = []
+ for set_id in sets:
+ coref_set = sets[set_id]
+ positives.extend(list(combinations(coref_set, 2)))
+ return positives
+
+
+def get_negatives_and_update_positives(all_mentions, positives, proportion):
+ all_pairs = list(combinations(all_mentions, 2))
+
+ all_pairs = set(all_pairs)
+ negatives = [pair for pair in all_pairs if pair not in positives]
+ samples_count = proportion * len(positives)
+ if samples_count > len(negatives):
+ samples_count = len(negatives)
+ if proportion == 1:
+ positives = random.sample(set(positives), samples_count)
+ print (u'Więcej przypadków pozytywnych niż negatywnych!')
+ negatives = random.sample(set(negatives), samples_count)
+ return positives, negatives
+
+
+def write_features(features_file, positives, negatives):
+ global POS_COUNT
+ POS_COUNT += len(positives)
+ for pair in positives:
+ vector = vectors.get_pair_vector(pair[0], pair[1])
+ vector.append(1.0)
+ features_file.write(u'%s\n' % u'\t'.join([str(feature) for feature in vector]))
+
+ global NEG_COUNT
+ NEG_COUNT += len(negatives)
+ for pair in negatives:
+ vector = vectors.get_pair_vector(pair[0], pair[1])
+ vector.append(0.0)
+ features_file.write(u'%s\n' % u'\t'.join([str(feature) for feature in vector]))
+
+
+if __name__ == '__main__':
+ main()
diff --git a/corneferencer/resolvers/features.py b/corneferencer/resolvers/features.py
index 987eae8..b4a83f0 100644
--- a/corneferencer/resolvers/features.py
+++ b/corneferencer/resolvers/features.py
@@ -72,97 +72,97 @@ def sentence_vec(mention):
def mention_type(mention):
- type_vec = [0] * 4
+ type_vec = [0.0] * 4
if mention.head is None:
- type_vec[3] = 1
+ type_vec[3] = 1.0
elif mention.head['ctag'] in constants.NOUN_TAGS:
- type_vec[0] = 1
+ type_vec[0] = 1.0
elif mention.head['ctag'] in constants.PPRON_TAGS:
- type_vec[1] = 1
+ type_vec[1] = 1.0
elif mention.head['ctag'] in constants.ZERO_TAGS:
- type_vec[2] = 1
+ type_vec[2] = 1.0
else:
- type_vec[3] = 1
+ type_vec[3] = 1.0
return type_vec
def is_first_second_person(mention):
if mention.head is None:
- return 0
+ return 0.0
if mention.head['person'] in constants.FIRST_SECOND_PERSON:
- return 1
- return 0
+ return 1.0
+ return 0.0
def is_demonstrative(mention):
if mention.words[0]['base'].lower() in constants.INDICATIVE_PRONS_BASES:
- return 1
- return 0
+ return 1.0
+ return 0.0
def is_demonstrative_nominal(mention):
if mention.head is None:
- return 0
+ return 0.0
if is_demonstrative(mention) and mention.head['ctag'] in constants.NOUN_TAGS:
- return 1
- return 0
+ return 1.0
+ return 0.0
def is_demonstrative_pronoun(mention):
if mention.head is None:
- return 0
+ return 0.0
if (is_demonstrative(mention) and
(mention.head['ctag'] in constants.PPRON_TAGS or mention.head['ctag'] in constants.ZERO_TAGS)):
- return 1
- return 0
+ return 1.0
+ return 0.0
def is_refl_pronoun(mention):
if mention.head is None:
- return 0
+ return 0.0
if mention.head['ctag'] in constants.SIEBIE_TAGS:
- return 1
- return 0
+ return 1.0
+ return 0.0
def is_first_in_sentence(mention):
if mention.first_in_sentence:
- return 1
- return 0
+ return 1.0
+ return 0.0
def is_zero_or_pronoun(mention):
if mention.head is None:
- return 0
+ return 0.0
if mention.head['ctag'] in constants.PPRON_TAGS or mention.head['ctag'] in constants.ZERO_TAGS:
- return 1
- return 0
+ return 1.0
+ return 0.0
def head_contains_digit(mention):
_digits = re.compile('\d')
if _digits.search(mention.head_orth):
- return 1
- return 0
+ return 1.0
+ return 0.0
def mention_contains_digit(mention):
_digits = re.compile('\d')
if _digits.search(mention.text):
- return 1
- return 0
+ return 1.0
+ return 0.0
def contains_letter(mention):
if any(c.isalpha() for c in mention.text):
- return 1
- return 0
+ return 1.0
+ return 0.0
def post_modified(mention):
if mention.head_orth != mention.words[-1]['orth']:
- return 1
- return 0
+ return 1.0
+ return 0.0
# pair features
@@ -171,20 +171,20 @@ def distances_vec(ante, ana):
mnts_intersect = pair_intersect(ante, ana)
- words_dist = [0] * 11
+ words_dist = [0.0] * 11
words_bucket = 0
- if mnts_intersect != 1:
+ if mnts_intersect != 1.0:
words_bucket = get_distance_bucket(ana.start_in_words - ante.end_in_words)
- words_dist[words_bucket] = 1
+ words_dist[words_bucket] = 1.0
vec.extend(words_dist)
- mentions_dist = [0] * 11
+ mentions_dist = [0.0] * 11
mentions_bucket = 0
- if mnts_intersect != 1:
+ if mnts_intersect != 1.0:
mentions_bucket = get_distance_bucket(ana.position_in_mentions - ante.position_in_mentions)
if words_bucket == 10:
mentions_bucket = 10
- mentions_dist[mentions_bucket] = 1
+ mentions_dist[mentions_bucket] = 1.0
vec.extend(mentions_dist)
vec.append(mnts_intersect)
@@ -196,45 +196,45 @@ def pair_intersect(ante, ana):
for ante_word in ante.words:
for ana_word in ana.words:
if ana_word['id'] == ante_word['id']:
- return 1
- return 0
+ return 1.0
+ return 0.0
def head_match(ante, ana):
if ante.head_orth.lower() == ana.head_orth.lower():
- return 1
- return 0
+ return 1.0
+ return 0.0
def exact_match(ante, ana):
if ante.text.lower() == ana.text.lower():
- return 1
- return 0
+ return 1.0
+ return 0.0
def base_match(ante, ana):
if ante.lemmatized_text.lower() == ana.lemmatized_text.lower():
- return 1
- return 0
+ return 1.0
+ return 0.0
def ante_contains_rarest_from_ana(ante, ana):
ana_rarest = ana.rarest
for word in ante.words:
if word['base'] == ana_rarest['base']:
- return 1
- return 0
+ return 1.0
+ return 0.0
def agreement(ante, ana, tag_name):
- agr_vec = [0] * 3
+ agr_vec = [0.0] * 3
if (ante.head is None or ana.head is None or
ante.head[tag_name] == 'unk' or ana.head[tag_name] == 'unk'):
- agr_vec[2] = 1
+ agr_vec[2] = 1.0
elif ante.head[tag_name] == ana.head[tag_name]:
- agr_vec[0] = 1
+ agr_vec[0] = 1.0
else:
- agr_vec[1] = 1
+ agr_vec[1] = 1.0
return agr_vec
@@ -243,72 +243,72 @@ def is_acronym(ante, ana):
return check_one_way_acronym(ana.text, ante.text)
if ante.text.upper() == ante.text:
return check_one_way_acronym(ante.text, ana.text)
- return 0
+ return 0.0
def same_sentence(ante, ana):
if ante.sentence_id == ana.sentence_id:
- return 1
- return 0
+ return 1.0
+ return 0.0
def neighbouring_sentence(ante, ana):
if ana.sentence_id - ante.sentence_id == 1:
- return 1
- return 0
+ return 1.0
+ return 0.0
def cousin_sentence(ante, ana):
if ana.sentence_id - ante.sentence_id == 2:
- return 1
- return 0
+ return 1.0
+ return 0.0
def distant_sentence(ante, ana):
if ana.sentence_id - ante.sentence_id > 2:
- return 1
- return 0
+ return 1.0
+ return 0.0
def same_paragraph(ante, ana):
if ante.paragraph_id == ana.paragraph_id:
- return 1
- return 0
+ return 1.0
+ return 0.0
def flat_gender_agreement(ante, ana):
- agr_vec = [0] * 3
+ agr_vec = [0.0] * 3
if (ante.head is None or ana.head is None or
ante.head['gender'] == 'unk' or ana.head['gender'] == 'unk'):
- agr_vec[2] = 1
+ agr_vec[2] = 1.0
elif (ante.head['gender'] == ana.head['gender'] or
(ante.head['gender'] in constants.MASCULINE_TAGS and ana.head['gender'] in constants.MASCULINE_TAGS)):
- agr_vec[0] = 1
+ agr_vec[0] = 1.0
else:
- agr_vec[1] = 1
+ agr_vec[1] = 1.0
return agr_vec
def left_match(ante, ana):
if (ante.text.lower().startswith(ana.text.lower()) or
ana.text.lower().startswith(ante.text.lower())):
- return 1
- return 0
+ return 1.0
+ return 0.0
def right_match(ante, ana):
if (ante.text.lower().endswith(ana.text.lower()) or
ana.text.lower().endswith(ante.text.lower())):
- return 1
- return 0
+ return 1.0
+ return 0.0
def abbrev2(ante, ana):
ante_abbrev = get_abbrev(ante)
ana_abbrev = get_abbrev(ana)
if ante.head_orth == ana_abbrev or ana.head_orth == ante_abbrev:
- return 1
- return 0
+ return 1.0
+ return 0.0
def string_kernel(ante, ana):
@@ -326,7 +326,7 @@ def head_string_kernel(ante, ana):
def wordnet_synonyms(ante, ana):
ante_synonyms = set()
if ante.head is None or ana.head is None:
- return 0
+ return 0.0
if ante.head['base'] in conf.LEMMA2SYNONYMS:
ante_synonyms = conf.LEMMA2SYNONYMS[ante.head['base']]
@@ -336,13 +336,13 @@ def wordnet_synonyms(ante, ana):
ana_synonyms = conf.LEMMA2SYNONYMS[ana.head['base']]
if ana.head['base'] in ante_synonyms or ante.head['base'] in ana_synonyms:
- return 1
- return 0
+ return 1.0
+ return 0.0
def wordnet_ana_is_hypernym(ante, ana):
if ante.head is None or ana.head is None:
- return 0
+ return 0.0
ante_hypernyms = set()
if ante.head['base'] in conf.LEMMA2HYPERNYMS:
@@ -353,16 +353,16 @@ def wordnet_ana_is_hypernym(ante, ana):
ana_hypernyms = conf.LEMMA2HYPERNYMS[ana.head['base']]
if not ante_hypernyms or not ana_hypernyms:
- return 0
+ return 0.0
if ana.head['base'] in ante_hypernyms:
- return 1
- return 0
+ return 1.0
+ return 0.0
def wordnet_ante_is_hypernym(ante, ana):
if ante.head is None or ana.head is None:
- return 0
+ return 0.0
ana_hypernyms = set()
if ana.head['base'] in conf.LEMMA2HYPERNYMS:
@@ -373,18 +373,18 @@ def wordnet_ante_is_hypernym(ante, ana):
ante_hypernyms = conf.LEMMA2HYPERNYMS[ante.head['base']]
if not ante_hypernyms or not ana_hypernyms:
- return 0
+ return 0.0
if ante.head['base'] in ana_hypernyms:
- return 1
- return 0
+ return 1.0
+ return 0.0
def wikipedia_link(ante, ana):
ante_base = ante.lemmatized_text.lower()
ana_base = ana.lemmatized_text.lower()
if ante_base == ana_base:
- return 1
+ return 1.0
ante_links = set()
if ante_base in conf.TITLE2LINKS:
@@ -395,16 +395,16 @@ def wikipedia_link(ante, ana):
ana_links = conf.TITLE2LINKS[ana_base]
if ana_base in ante_links or ante_base in ana_links:
- return 1
+ return 1.0
- return 0
+ return 0.0
def wikipedia_mutual_link(ante, ana):
ante_base = ante.lemmatized_text.lower()
ana_base = ana.lemmatized_text.lower()
if ante_base == ana_base:
- return 1
+ return 1.0
ante_links = set()
if ante_base in conf.TITLE2LINKS:
@@ -415,52 +415,52 @@ def wikipedia_mutual_link(ante, ana):
ana_links = conf.TITLE2LINKS[ana_base]
if ana_base in ante_links and ante_base in ana_links:
- return 1
+ return 1.0
- return 0
+ return 0.0
def wikipedia_redirect(ante, ana):
ante_base = ante.lemmatized_text.lower()
ana_base = ana.lemmatized_text.lower()
if ante_base == ana_base:
- return 1
+ return 1.0
if ante_base in conf.TITLE2REDIRECT and conf.TITLE2REDIRECT[ante_base] == ana_base:
- return 1
+ return 1.0
if ana_base in conf.TITLE2REDIRECT and conf.TITLE2REDIRECT[ana_base] == ante_base:
- return 1
+ return 1.0
- return 0
+ return 0.0
def samesent_anapron_antefirstinpar(ante, ana):
if same_sentence(ante, ana) and is_zero_or_pronoun(ana) and ante.first_in_paragraph:
- return 1
- return 0
+ return 1.0
+ return 0.0
def samesent_antefirstinpar_personnumbermatch(ante, ana):
if (same_sentence(ante, ana) and ante.first_in_paragraph
and agreement(ante, ana, 'number')[0] and agreement(ante, ana, 'person')[0]):
- return 1
- return 0
+ return 1.0
+ return 0.0
def adjsent_anapron_adjmen_personnumbermatch(ante, ana):
if (neighbouring_sentence(ante, ana) and is_zero_or_pronoun(ana)
and ana.position_in_mentions - ante.position_in_mentions == 1
and agreement(ante, ana, 'number')[0] and agreement(ante, ana, 'person')[0]):
- return 1
- return 0
+ return 1.0
+ return 0.0
def adjsent_anapron_adjmen(ante, ana):
if (neighbouring_sentence(ante, ana) and is_zero_or_pronoun(ana)
and ana.position_in_mentions - ante.position_in_mentions == 1):
- return 1
- return 0
+ return 1.0
+ return 0.0
# supporting functions
@@ -523,8 +523,8 @@ def check_one_way_acronym(acronym, expression):
if expr2:
initials += expr2[0].upper()
if acronym == initials:
- return 1
- return 0
+ return 1.0
+ return 0.0
def get_abbrev(mention):
diff --git a/corneferencer/resolvers/resolve.py b/corneferencer/resolvers/resolve.py
index c3a1038..bcca721 100644
--- a/corneferencer/resolvers/resolve.py
+++ b/corneferencer/resolvers/resolve.py
@@ -1,17 +1,15 @@
import numpy
-from conf import NEURAL_MODEL
-from corneferencer.resolvers import features
-from corneferencer.resolvers.vectors import get_pair_features, get_pair_vector
+from corneferencer.resolvers import features, vectors
-def siamese(text, threshold):
+def siamese(text, threshold, neural_model):
last_set_id = 0
for i, ana in enumerate(text.mentions):
if i > 0:
for ante in reversed(text.mentions[:i]):
if not features.pair_intersect(ante, ana):
- pair_features = get_pair_features(ante, ana)
+ pair_features = vectors.get_pair_features(ante, ana)
ante_vec = []
ante_vec.extend(ante.features)
@@ -23,7 +21,7 @@ def siamese(text, threshold):
ana_vec.extend(pair_features)
ana_sample = numpy.asarray([ana_vec], dtype=numpy.float32)
- prediction = NEURAL_MODEL.predict([ante_sample, ana_sample])[0]
+ prediction = neural_model.predict([ante_sample, ana_sample])[0]
if prediction < threshold:
if ante.set:
@@ -37,7 +35,7 @@ def siamese(text, threshold):
# incremental resolve algorithm
-def incremental(text, threshold):
+def incremental(text, threshold, neural_model):
last_set_id = 0
for i, ana in enumerate(text.mentions):
if i > 0:
@@ -45,9 +43,9 @@ def incremental(text, threshold):
best_ante = None
for ante in text.mentions[:i]:
if not features.pair_intersect(ante, ana):
- pair_vec = get_pair_vector(ante, ana)
+ pair_vec = vectors.get_pair_vector(ante, ana)
sample = numpy.asarray([pair_vec], dtype=numpy.float32)
- prediction = NEURAL_MODEL.predict(sample)[0]
+ prediction = neural_model.predict(sample)[0]
if prediction > threshold and prediction >= best_prediction:
best_prediction = prediction
best_ante = ante
@@ -62,86 +60,21 @@ def incremental(text, threshold):
# all2all resolve algorithm
-def all2all_debug(text, threshold):
- last_set_id = 0
- for pos1, mnt1 in enumerate(text.mentions):
- best_prediction = 0.0
- best_link = None
- for pos2, mnt2 in enumerate(text.mentions):
- if (mnt1.set != mnt2.set or not mnt1.set) and pos1 != pos2 and not features.pair_intersect(mnt1, mnt2):
- ante = mnt1
- ana = mnt2
- if pos2 < pos1:
- ante = mnt2
- ana = mnt1
- pair_vec = get_pair_vector(ante, ana)
- sample = numpy.asarray([pair_vec], dtype=numpy.float32)
- prediction = NEURAL_MODEL.predict(sample)[0]
- if prediction > threshold and prediction > best_prediction:
- best_prediction = prediction
- best_link = mnt2
- if best_link is not None:
- if best_link.set and not mnt1.set:
- mnt1.set = best_link.set
- elif best_link.set and mnt1.set:
- text.merge_sets(best_link.set, mnt1.set)
- elif not best_link.set and not mnt1.set:
- str_set_id = 'set_%d' % last_set_id
- best_link.set = str_set_id
- mnt1.set = str_set_id
- last_set_id += 1
-
-
-def all2all_v1(text, threshold):
- last_set_id = 0
- for pos1, mnt1 in enumerate(text.mentions):
- best_prediction = 0.0
- best_link = None
- for pos2, mnt2 in enumerate(text.mentions):
- if ((mnt1.set != mnt2.set or not mnt1.set or not mnt2.set)
- and pos1 != pos2 and not features.pair_intersect(mnt1, mnt2)):
- ante = mnt1
- ana = mnt2
- if pos2 < pos1:
- ante = mnt2
- ana = mnt1
- pair_vec = get_pair_vector(ante, ana)
- sample = numpy.asarray([pair_vec], dtype=numpy.float32)
- prediction = NEURAL_MODEL.predict(sample)[0]
- if prediction > threshold and prediction > best_prediction:
- best_prediction = prediction
- best_link = mnt2
- if best_link is not None:
- if best_link.set and not mnt1.set:
- mnt1.set = best_link.set
- elif not best_link.set and mnt1.set:
- best_link.set = mnt1.set
- elif best_link.set and mnt1.set:
- text.merge_sets(best_link.set, mnt1.set)
- elif not best_link.set and not mnt1.set:
- str_set_id = 'set_%d' % last_set_id
- best_link.set = str_set_id
- mnt1.set = str_set_id
- last_set_id += 1
-
-
-def all2all(text, threshold):
+def all2all(text, threshold, neural_model):
last_set_id = 0
sets = text.get_sets()
for pos1, mnt1 in enumerate(text.mentions):
best_prediction = 0.0
best_link = None
for pos2, mnt2 in enumerate(text.mentions):
- if ((mnt1.set != mnt2.set or not mnt1.set or not mnt2.set)
- and pos1 != pos2 and not features.pair_intersect(mnt1, mnt2)):
+ if (pos2 > pos1 and
+ (mnt1.set != mnt2.set or not mnt1.set or not mnt2.set)
+ and not features.pair_intersect(mnt1, mnt2)):
ante = mnt1
ana = mnt2
- if pos2 < pos1:
- ante = mnt2
- ana = mnt1
- pair_vec = get_pair_vector(ante, ana)
+ pair_vec = vectors.get_pair_vector(ante, ana)
sample = numpy.asarray([pair_vec], dtype=numpy.float32)
- prediction = NEURAL_MODEL.predict(sample)[0]
+ prediction = neural_model.predict(sample)[0]
if prediction > threshold and prediction > best_prediction:
best_prediction = prediction
best_link = mnt2
@@ -163,12 +96,12 @@ def all2all(text, threshold):
# entity based resolve algorithm
-def entity_based(text, threshold):
+def entity_based(text, threshold, neural_model):
sets = []
last_set_id = 0
for i, ana in enumerate(text.mentions):
if i > 0:
- best_fit = get_best_set(sets, ana, threshold)
+ best_fit = get_best_set(sets, ana, threshold, neural_model)
if best_fit is not None:
ana.set = best_fit['set_id']
best_fit['mentions'].append(ana)
@@ -188,25 +121,25 @@ def entity_based(text, threshold):
remove_singletons(sets)
-def get_best_set(sets, ana, threshold):
+def get_best_set(sets, ana, threshold, neural_model):
best_prediction = 0.0
best_set = None
for s in sets:
- accuracy = predict_set(s['mentions'], ana)
+ accuracy = predict_set(s['mentions'], ana, neural_model)
if accuracy > threshold and accuracy >= best_prediction:
best_prediction = accuracy
best_set = s
return best_set
-def predict_set(mentions, ana):
+def predict_set(mentions, ana, neural_model):
prediction_sum = 0.0
for mnt in mentions:
prediction = 0.0
if not features.pair_intersect(mnt, ana):
- pair_vec = get_pair_vector(mnt, ana)
+ pair_vec = vectors.get_pair_vector(mnt, ana)
sample = numpy.asarray([pair_vec], dtype=numpy.float32)
- prediction = NEURAL_MODEL.predict(sample)[0]
+ prediction = neural_model.predict(sample)[0]
prediction_sum += prediction
return prediction_sum / float(len(mentions))
@@ -218,15 +151,15 @@ def remove_singletons(sets):
# closest resolve algorithm
-def closest(text, threshold):
+def closest(text, threshold, neural_model):
last_set_id = 0
for i, ana in enumerate(text.mentions):
if i > 0:
for ante in reversed(text.mentions[:i]):
if not features.pair_intersect(ante, ana):
- pair_vec = get_pair_vector(ante, ana)
+ pair_vec = vectors.get_pair_vector(ante, ana)
sample = numpy.asarray([pair_vec], dtype=numpy.float32)
- prediction = NEURAL_MODEL.predict(sample)[0]
+ prediction = neural_model.predict(sample)[0]
if prediction > threshold:
if ante.set:
ana.set = ante.set
diff --git a/corneferencer/utils.py b/corneferencer/utils.py
index ea6b125..15c4ca1 100644
--- a/corneferencer/utils.py
+++ b/corneferencer/utils.py
@@ -7,7 +7,6 @@ import javaobj
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Dropout, Activation, BatchNormalization, Lambda
-from keras.optimizers import RMSprop, Adam
from keras import backend as K