def separate_dataset(self, in_file, out_file, check_function):
"""
This function is used to separate the original word similarity dataset.
word similarity of noun: noun_rg.txt, noun_mc.txt, noun_ws353.txt, noun_ws353-sim.txt, noun_simlex.txt
the lcs is in knowledge graph: graph_rg.txt, graph_mc.txt, graph_ws353.txt,
graph_ws353-sim.txt, graph_simlex.txt
both words are in knowledge graph: type_rg.txt, type_mc.txt, type_ws353.txt, type_ws353-sim.txt,
type_simlex.txt
:param in_file: source dataset file
:param out_file: target dataset file
:param check_function: the function of mapping criteria for deciding the word pairs.
:return:
"""
out_data = []
word_pairs, human = self.load_dataset(in_file)
for i, pairs in enumerate(word_pairs):
w1, w2 = pairs
h = human[i]
if check_function(w1, w2):
out_data.append(' '.join([w1, w2, str(h)]))
FileIO.save_list_file('dataset/wordsim/%s.txt' % out_file, out_data)
def separate_dataset(self, in_file, out_file, check_function):
"""
This function is used to separate the original word similarity dataset.
word similarity of noun: noun_rg.txt, noun_mc.txt, noun_ws353.txt, noun_ws353-sim.txt, noun_simlex.txt
the lcs is in knowledge graph: graph_rg.txt, graph_mc.txt, graph_ws353.txt,
graph_ws353-sim.txt, graph_simlex.txt
both words are in knowledge graph: type_rg.txt, type_mc.txt, type_ws353.txt, type_ws353-sim.txt,
type_simlex.txt
:param in_file: source dataset file
:param out_file: target dataset file
:param check_function: the function of mapping criteria for deciding the word pairs.
:return:
"""
out_data = []
word_pairs, human = self.load_dataset(in_file)
for i, pairs in enumerate(word_pairs):
w1, w2 = pairs
h = human[i]
if check_function(w1, w2):
out_data.append(' '.join([w1, w2, str(h)]))
FileIO.save_list_file('eval/word_similarity/%s.txt' % out_file, out_data)
def __init__(self,
DICT='models/abstract/abstracts.dict',
TFIDF_MODEL='models/abstract/abstracts_tfidf.model',
LSA_MODEL='models/abstract/abstracts_lsi.model'):
try:
from nltk.tokenize import RegexpTokenizer
from nltk.stem import WordNetLemmatizer
import nltk
self._tokenizer = RegexpTokenizer(r'[a-z]+')
self._lemma = WordNetLemmatizer()
self._stopwords = set(nltk.corpus.stopwords.words('english'))
except:
print('Install NLTK and download WordNet!')
import sys
sys.exit()
try:
from gensim import corpora, models
from sematch.utility import FileIO
self._dict = corpora.Dictionary.load(FileIO.filename(DICT))
self._tfidf = models.TfidfModel.load(FileIO.filename(TFIDF_MODEL))
self._lsa = models.LsiModel.load(FileIO.filename(LSA_MODEL))
except:
print('Install gensim and prepare models data!')
import sys
sys.exit()
def graph_ic_writer(self, filename, data):
"""
Save the ic values for a concept for faster access.
:param filename:
:param data:
:return:
"""
FileIO.append_json_file(filename, data)
def graph_ic_writer(self, filename, data):
"""
Save the ic values for a concept for faster access.
:param filename:
:param data:
:return:
"""
FileIO.append_json_file(filename, data)
def test_embedding():
from gensim.models import KeyedVectors
from sematch.utility import FileIO
from sematch.semantic.relatedness import WordRelatedness
model_wiki = KeyedVectors.load_word2vec_format(FileIO.filename('models/w2v-model-enwiki_w2vformat'), binary=True)
model_news = KeyedVectors.load_word2vec_format(FileIO.filename('models/googlenews.bin'), binary=True)
rel = WordRelatedness(model_news)
print(rel.word_similarity('happy','sad'))
def test_evaluation():
from sematch.utility import FileIO
query = FileIO.read_json_file('dataset/ned/query_ned_cleaned.txt')
question = FileIO.read_json_file('dataset/ned/question_ned_cleaned.txt')
tweet = FileIO.read_json_file('dataset/ned/tweet_ned_cleaned.txt')
print len(query)
print len(question)
print len(tweet)
def test_evaluation():
from sematch.utility import FileIO
query = FileIO.read_json_file('dataset/ned/query_ned_cleaned.txt')
question = FileIO.read_json_file('dataset/ned/question_ned_cleaned.txt')
tweet = FileIO.read_json_file('dataset/ned/tweet_ned_cleaned.txt')
print len(query)
print len(question)
print len(tweet)
def test_entity_feature():
from sematch.utility import FileIO
from sematch.nlp import EntityFeature
query = FileIO.read_json_file('dataset/ned/query_ned_cleaned.txt')
question = FileIO.read_json_file('dataset/ned/question_ned_cleaned.txt')
tweet = FileIO.read_json_file('dataset/ned/tweet_ned_cleaned.txt')
import itertools
candidates = list(itertools.chain.from_iterable(map(lambda x:x['candidate'], question)))
set_candidates = list(set(candidates))
print len(set_candidates)
EntityFeature.candidate_features(set_candidates, export_file='models/question_features.json')
def save_result(self, cor, sim_values, sim_name, dataset_name):
"""
This function save the result computed by a similarity metric
:param cor: correlation with human rating
:param sim_values: similarity scores for word pairs
:param sim_name: the name of similarity metric
:param dataset_name: the name of word similarity dataset
:return:
"""
data = ["%.3f" % cor]
data += map(lambda x: "%.3f" % x, sim_values)
FileIO.save_list_file('eval/word_similarity/results/%s-%s.txt' % (dataset_name, sim_name), data)
def save_result(self, cor, sim_values, sim_name, dataset_name):
"""
This function save the result computed by a similarity metric
:param cor: correlation with human rating
:param sim_values: similarity scores for word pairs
:param sim_name: the name of similarity metric
:param dataset_name: the name of word similarity dataset
:return:
"""
data = ["%.3f" % cor]
data += map(lambda x: "%.3f" % x, sim_values)
FileIO.save_list_file('dataset/wordsim/results/%s-%s.txt' % (dataset_name, sim_name), data)
def evaluate(self, input_file, output_file):
"""
Evaluate the sentence similarity
:param input_file: corpus file
:param output_file: result file
:return: similarity scores of text pairs
"""
corpus = self.load_dataset(input_file)
print 'dataset size: ', len(corpus)
result = [self._sim_metric(t1, t2) for t1, t2 in corpus]
result = map(lambda x: "%.3f" % round(x, 3), result)
FileIO.save_list_file(output_file, result)
return result
def evaluate(self, input_file, output_file):
"""
Evaluate the sentence similarity
:param input_file: corpus file
:param output_file: result file
:return: similarity scores of text pairs
"""
corpus = self.load_dataset(input_file)
print 'dataset size: ', len(corpus)
result = [self._sim_metric(t1, t2) for t1, t2 in corpus]
result = map(lambda x:"%.3f" % round(x,3), result)
FileIO.save_list_file(output_file, result)
return result
def candidate_features(cls, candidates, export_file='models/candidate_features.json',
feature_dict_file='models/entity_features.json'):
from sematch.utility import FileIO
entity_features = FileIO.read_json_file(feature_dict_file)
entity_features = {e['dbr']: (e['desc'], e['cat']) for e in entity_features}
features = []
for i, can in enumerate(candidates):
print i, " ", can
data = {}
data['dbr'] = can
data['desc'] = entity_features[can][0] if can in entity_features else None
data['cat'] = entity_features[can][1] if can in entity_features else []
features.append(data)
FileIO.save_json_file(export_file, features)
return features
def test_category():
from gensim.models.doc2vec import Doc2Vec
from sematch.utility import FileIO
from sematch.semantic.relatedness import ConceptRelatedness
model_category = Doc2Vec.load(FileIO.filename('models/category/cat2vec'))
cat2vec_rel = ConceptRelatedness(model_category)
print(cat2vec_rel.word_similarity('happy','sad'))
def __init__(self, vec_file='models/GoogleNews-vectors-negative300.bin', binary=True):
"""
:param vec_file: the file storing vectors
:param binary: if vector are stored in binary. Google news use binary while yelp not
"""
self._wordvec = Word2Vec.load_word2vec_format(FileIO.filename(vec_file), binary=binary)
def graph_ic_reader(self, filename):
"""
Load the saved IC values
:param filename: the file containing IC values of concepts
:return: a dictionary concept:IC
"""
data = FileIO.read_json_file(filename)
return {d['concept']:float(d['ic']) for d in data}
def load(cls, feature_dict_file='models/entity_features.json'):
from sematch.utility import FileIO
entity_features = FileIO.read_json_file(feature_dict_file)
entity_features = {
e['dbr']: (e['desc'], e['cat'])
for e in entity_features
}
return cls(entity_features)
def graph_ic_reader(self, filename):
"""
Load the saved IC values
:param filename: the file containing IC values of concepts
:return: a dictionary concept:IC
"""
data = FileIO.read_json_file(filename)
return {d['concept']: float(d['ic']) for d in data}
def __init__(self, src='models/dbpedia_2015-04.owl'):
self.graph = rdflib.Graph()
self.graph.parse(FileIO.filename(src))
self.root = 'http://www.w3.org/2002/07/owl#Thing'
self.classes = [s for s in self.graph.subjects(RDF.type, OWL.Class)]
self.o_properties = [s for s in self.graph.subjects(RDF.type, OWL.ObjectProperty)]
self.d_properties = [s for s in self.graph.subjects(RDF.type, OWL.DatatypeProperty)]
self.uri2class = {c.toPython():c for c in self.classes}
self.uri2class[self.root] = rdflib.URIRef(self.root)
self.class_labels = [self.token(c) for c in self.classes]
def __init__(self,
vec_file='models/GoogleNews-vectors-negative300.bin',
binary=True):
"""
:param vec_file: the file storing vectors
:param binary: if vector are stored in binary. Google news use binary while yelp not
"""
self._wordvec = Word2Vec.load_word2vec_format(
FileIO.filename(vec_file), binary=binary)
def __init__(self,
graph_ic='models/yago_type_ic.txt',
mappings="models/type-linkings.txt"):
WordNetSimilarity.__init__(self)
self._graph_ic = GraphIC(graph_ic)
self._mappings = FileIO.read_json_file(mappings)
self._id2mappings = {data['offset']: data for data in self._mappings}
self._yago2id = {
data['yago_dbpedia']: data['offset']
for data in self._mappings
}
def load_result(self, sim_name, dataset_name):
"""
This function loads the result of a similarity metric for a specific dataset
:param sim_name: the name similarity metric
:param dataset_name: the name of word similarity dataset
:return: cor relation score and rating scores generated by similarity metric
"""
data = FileIO.read_list_file('dataset/wordsim/results/%s-%s.txt' % (dataset_name, sim_name))
data = list(map(float, data))
return data[0], data[1:]
def load_result(self, sim_name, dataset_name):
"""
This function loads the result of a similarity metric for a specific dataset
:param sim_name: the name similarity metric
:param dataset_name: the name of word similarity dataset
:return: cor relation score and rating scores generated by similarity metric
"""
data = FileIO.read_list_file('eval/word_similarity/results/%s-%s.txt' % (dataset_name, sim_name))
data = map(float, data)
return data[0], data[1:]
def load_dataset(self, dataset_name):
"""
This function loads the word similarity dataset
:param dataset_name: the file name of word similarity dataset
:return: word pairs and huamn ratings
"""
data = FileIO.read_list_file('dataset/wordsim/%s.txt' % dataset_name)
#print "dataset ", dataset_name, " ", len(data), " word pairs"
word_pairs = map(lambda x: (x.split()[0], x.split()[1]), data)
human = list(map(float, map(lambda x: x.split()[2], data)))
return word_pairs, human
def load_dataset(self, dataset_name):
"""
This function loads the word similarity dataset
:param dataset_name: the file name of word similarity dataset
:return: word pairs and huamn ratings
"""
data = FileIO.read_list_file('eval/word_similarity/%s.txt' % dataset_name)
#print "dataset ", dataset_name, " ", len(data), " word pairs"
word_pairs = map(lambda x: (x.split()[0], x.split()[1]), data)
human = map(float, map(lambda x: x.split()[2], data))
return word_pairs, human
def load_dataset(self, dataset_file, cat_full=False):
from BeautifulSoup import BeautifulSOAP as bs
pairs = []
with open(FileIO.filename(dataset_file), 'r') as f:
corpus = f.read()
opinions = bs(corpus).findAll('opinion')
for op in opinions:
if not op['target'] == 'NULL':
t = op['target']
c = op['category'] if cat_full else op['category'].split('#')[0]
pairs.append((t, c))
X, y = zip(*pairs)
return X, y
def candidate_features(cls,
candidates,
export_file='models/candidate_features.json',
feature_dict_file='models/entity_features.json'):
from sematch.utility import FileIO
entity_features = FileIO.read_json_file(feature_dict_file)
entity_features = {
e['dbr']: (e['desc'], e['cat'])
for e in entity_features
}
features = []
for i, can in enumerate(candidates):
print i, " ", can
data = {}
data['dbr'] = can
data['desc'] = entity_features[can][
0] if can in entity_features else None
data['cat'] = entity_features[can][
1] if can in entity_features else []
features.append(data)
FileIO.save_json_file(export_file, features)
return features
def load_dataset(self, dataset_file):
"""
Generate sentence pairs.
:param dataset_file: dataset file
:return: sentence pairs
"""
data = FileIO.read_list_file(dataset_file)
data = [d.strip() for d in data]
corpus = []
for d in data:
item = d.split('\t')
corpus.append((item[0], item[1]))
return corpus
def load_dataset(self, dataset_file):
"""
Generate sentence pairs.
:param dataset_file: dataset file
:return: sentence pairs
"""
data = FileIO.read_list_file(dataset_file)
data = [d.strip() for d in data]
corpus = []
for d in data:
item = d.split('\t')
corpus.append((item[0], item[1]))
return corpus
def test_feature_extractor():
from sematch.nlp import FeatureExtractor
from sematch.nlp import EntityFeature
from sematch.nlp import SpaCyNLP
from sematch.utility import FileIO
import itertools
sy = SpaCyNLP()
w_extractor = FeatureExtractor(sy.pos_tag)
features = EntityFeature.load(feature_dict_file='models/query_features.json')
query = FileIO.read_json_file('dataset/ned/query_ned_cleaned.txt')
candidates = list(itertools.chain.from_iterable(map(lambda x: x['candidate'], query)))
set_candidates = list(set(candidates))
for can in set_candidates[:10]:
print w_extractor.entity_word_features([can], features)
def __init__(self, src='models/dbpedia_2015-04.owl'):
self.graph = rdflib.Graph()
self.graph.parse(FileIO.filename(src))
self.root = 'http://www.w3.org/2002/07/owl#Thing'
self.classes = [s for s in self.graph.subjects(RDF.type, OWL.Class)]
self.o_properties = [
s for s in self.graph.subjects(RDF.type, OWL.ObjectProperty)
]
self.d_properties = [
s for s in self.graph.subjects(RDF.type, OWL.DatatypeProperty)
]
self.uri2class = {c.toPython(): c for c in self.classes}
self.uri2class[self.root] = rdflib.URIRef(self.root)
self.class_labels = [self.token(c) for c in self.classes]
def load_dataset(self, dataset_file, cat_full=False):
from BeautifulSoup import BeautifulSOAP as bs
pairs = []
with open(FileIO.filename(dataset_file), 'r') as f:
corpus = f.read()
opinions = bs(corpus).findAll('opinion')
for op in opinions:
if not op['target'] == 'NULL':
t = op['target']
c = op['category'] if cat_full else op['category'].split(
'#')[0]
pairs.append((t, c))
X, y = zip(*pairs)
return X, y
def test_query_ned():
from sematch.nlp import FeatureExtractor
from sematch.nlp import EntityFeature
from sematch.nlp import SpaCyNLP
from sematch.utility import FileIO
from sematch.semantic.relatedness import TextRelatedness
from sematch.nel import EntityDisambiguation
import itertools
sy = SpaCyNLP()
features = EntityFeature.load(
feature_dict_file='models/query_features.json')
extractor = FeatureExtractor(features, sy.pos_tag)
ned = EntityDisambiguation(extractor)
rel = TextRelatedness()
from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()
#print wns.word_similarity('cooling', 'air_conditioner', 'li')
#similarity = lambda x,y : rel.text_similarity(x,y, model='lsa')
query = FileIO.read_json_file('dataset/ned/query_ned_cleaned.txt')
query = [q for q in query if extractor.context_features(q['query'])]
print len(query)
import warnings
warnings.filterwarnings("ignore")
metrics = ['path', 'wup', 'res', 'lin', 'jcn', 'wpath']
for m in metrics:
print m
similarity = lambda x, y: wns.word_similarity(x, y, m)
for k in range(1, 21):
gold = []
predict = []
for q in query:
gold.append(q['gold'])
#e = ned.text_disambiguate(q['query'], q['candidate'], similarity)
e = ned.word_disambiguate(q['query'],
q['candidate'],
similarity,
K=k)
predict.append(e)
from sklearn.metrics import precision_recall_fscore_support
#from sklearn.metrics import classification_report
#print classification_report(gold, predict)
print precision_recall_fscore_support(gold,
predict,
average='weighted')[2]
def test_query_ned():
from sematch.nlp import FeatureExtractor
from sematch.nlp import EntityFeature
from sematch.nlp import SpaCyNLP
from sematch.utility import FileIO
from sematch.semantic.relatedness import TextRelatedness
from sematch.nel import EntityDisambiguation
import itertools
sy = SpaCyNLP()
features = EntityFeature.load(feature_dict_file='models/query_features.json')
extractor = FeatureExtractor(features, sy.pos_tag)
ned = EntityDisambiguation(extractor)
rel = TextRelatedness()
from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()
#print wns.word_similarity('cooling', 'air_conditioner', 'li')
#similarity = lambda x,y : rel.text_similarity(x,y, model='lsa')
query = FileIO.read_json_file('dataset/ned/query_ned_cleaned.txt')
query = [q for q in query if extractor.context_features(q['query'])]
print len(query)
import warnings
warnings.filterwarnings("ignore")
metrics = ['path', 'wup', 'res', 'lin', 'jcn', 'wpath']
for m in metrics:
print m
similarity = lambda x, y: wns.word_similarity(x, y, m)
for k in range(1, 21):
gold = []
predict = []
for q in query:
gold.append(q['gold'])
#e = ned.text_disambiguate(q['query'], q['candidate'], similarity)
e = ned.word_disambiguate(q['query'], q['candidate'], similarity, K=k)
predict.append(e)
from sklearn.metrics import precision_recall_fscore_support
#from sklearn.metrics import classification_report
#print classification_report(gold, predict)
print precision_recall_fscore_support(gold, predict, average='weighted')[2]
def load(cls, name_dict_file='models/name.dict'):
from sematch.utility import FileIO
name = FileIO.read_json_file(name_dict_file)
name = {n['name']: n['concepts'] for n in name}
return cls(name)
def load(cls, feature_dict_file='models/entity_features.json'):
from sematch.utility import FileIO
entity_features = FileIO.read_json_file(feature_dict_file)
entity_features = {e['dbr']: (e['desc'], e['cat']) for e in entity_features}
return cls(entity_features)
def load(cls, name_dict_file='models/name.dict'):
from sematch.utility import FileIO
name = FileIO.read_json_file(name_dict_file)
name = {n['name']: n['concepts'] for n in name}
return cls(name)
def load_stopwords(self, filename):
data = FileIO.read_list_file(FileIO.filename(filename))
data = [d.split() for d in data[1:]] # skip first line, in case more than one word per line
data = list(itertools.chain.from_iterable(data))
return data
def __init__(self, graph_ic='models/yago_type_ic.txt', mappings="models/type-linkings.txt"):
WordNetSimilarity.__init__(self)
self._graph_ic = GraphIC(graph_ic)
self._mappings = FileIO.read_json_file(mappings)
self._id2mappings = {data['offset']: data for data in self._mappings}
self._yago2id = {data['yago_dbpedia']: data['offset'] for data in self._mappings}
def load_dataset(self):
data = FileIO.read_json_file('dataset/aspect/data.txt')
X, y = zip(*[(d['text'], d['label']) for d in data])
return X, y
def load_dataset(self):
data = FileIO.read_json_file('dataset/aspect/data.txt')
X, y = zip(*[(d['text'], d['label']) for d in data])
return X, y
def load_stopwords(self, filename):
data = FileIO.read_list_file(FileIO.filename(filename))
data = [d.split() for d in data[1:]] # skip first line, in case more than one word per line
data = list(itertools.chain.from_iterable(data))
return data