def noContextSim(self, tokens, trained_model):
print('Running: Word Similarity - Context False')
synset_op = SynsetParserVector()
new_tokens = []
for token in tokens:
new_token = TokenData()
synsets_a = wordnet.synsets(token.word1)
synsets_b = wordnet.synsets(token.word2)
# clean synsets that only exist in the model
vec_syna = synset_op.validate_synsets_model(
token.word1, synsets_a, trained_model)
vec_synb = synset_op.validate_synsets_model(
token.word2, synsets_b, trained_model)
# perform all metrics Word Similarity
new_token.sim.max = self.maxSim(vec_syna, vec_synb)
new_token.sim.avg = self.avgSim(vec_syna, vec_synb)
new_token.sim.glob = self.globalSim(vec_syna, vec_synb)
new_token.word1 = token.word1
new_token.word2 = token.word2
new_tokens.append(new_token)
return new_tokens
def yesContextSim(self, tokens, trained_model):
print('Running: Word Similarity - Context True')
sim_nocontext = NoContextSimilarity()
synset_op = SynsetParserVector()
text_parser = TextParser()
new_tokens = []
for token in tokens:
new_token = TokenData()
synsets_a = wordnet.synsets(token.word1)
synsets_b = wordnet.synsets(token.word2)
# clean and tokenizer context
context_a = text_parser.cleanText(token.sent1)
context_b = text_parser.cleanText(token.sent2)
# average vector for the context for each word
clean_context_a = self.__contextParser(context_a, trained_model)
clean_context_b = self.__contextParser(context_b, trained_model)
# clean synsets that only exist in the model
vec_syna = synset_op.validate_synsets_model(
token.word1, synsets_a, trained_model)
vec_synb = synset_op.validate_synsets_model(
token.word2, synsets_b, trained_model)
# perform all metrics Word Similarity
# Context
new_token.sim.maxC = self.__maxSimC(vec_syna, clean_context_a,
vec_synb, clean_context_b)
new_token.sim.avgC = self.__avgSimC(vec_syna, clean_context_a,
vec_synb, clean_context_b)
new_token.sim.globC = self.__globalSimC(clean_context_a,
clean_context_b)
# No Context
new_token.sim.max = sim_nocontext.maxSim(vec_syna, vec_synb)
new_token.sim.avg = sim_nocontext.avgSim(vec_syna, vec_synb)
new_token.sim.glob = sim_nocontext.globalSim(vec_syna, vec_synb)
new_token.word1 = token.word1
new_token.word2 = token.word2
new_tokens.append(new_token)
return new_tokens
def convert_SCWS(self, input_file, output_file, delimiter='\t'):
fio = FileManipulation()
ranges = RangeData()
tokens = []
with open(input_file, 'r', encoding='utf-8') as fin:
for line in fin:
token = TokenData()
block = line.split(delimiter)
token.word1 = block[1]
token.word2 = block[3]
token.sent1 = block[5] # target word1 between <b> </b>
token.sent2 = block[6] # target word2 between <b> </b>
token.simvalue = float(block[7].strip('\n'))
new_range = ranges.range_category['cos']
old_range = ranges.range_category['scws']
token.simvalue = numpy.interp(token.simvalue, old_range, new_range)
tokens.append(token)
fio.writeSimilarityContext(output_file, tokens) # specific writer for SCWS
def readFileLine(self, fname, delimiter='\t'):
tokens = []
record_items = 3
with open(fname, 'r', encoding='utf-8') as fin:
for line in fin:
token = TokenData()
record = line.split(delimiter)
token.word1 = record[0]
token.word2 = record[1]
if len(record) == record_items: # w1 w2 sim (3)
token.simvalue = float(record[2].strip('\n'))
else: # w1 w2 s1 s2 sim (5)
token.sent1 = record[2]
token.sent2 = record[3]
token.simvalue = float(record[4].strip('\n'))
tokens.append(token)
return tokens
def convert_MC28(self, input_file, output_file, delimiter=';'):
fio = FileManipulation()
ranges = RangeData()
tokens = []
with open(input_file, 'r', encoding='utf-8') as fin:
for line in fin:
token = TokenData()
block = line.split(delimiter)
token.word1 = block[0]
token.word2 = block[1]
token.simvalue = float(block[2].strip('\n'))
new_range = ranges.range_category['cos']
old_range = ranges.range_category['mc28']
token.simvalue = numpy.interp(token.simvalue, old_range, new_range)
tokens.append(token)
fio.writeSimilarityWord(output_file, tokens)