class NLPPhraseSimMeasurer(CompoundPhraseSimMeasurer):
def __init__(self, brown_ic, search_query):
CompoundPhraseSimMeasurer.__init__(self, search_query)
self.brown_ic = brown_ic
self.search_POS_map = self.load_POS_map(search_query)
self.word_measurer = SemanticWordSimMeasurer(brown_ic, self.search_words)
# Use the NLTK tool to parse a phrase and get each word's POS tag
def load_POS_map(self, phrase):
POS_map = {}
POS_tokens = nltk.word_tokenize(phrase)
POS_tags = nltk.pos_tag(POS_tokens)
for tag in POS_tags:
POS_map[tag[0]] = tag[1]
return POS_map
# Measure the semantic similarity between search query and response text from spotify
# by using the semantic nets of two words
def measure_phrase_sim(self, response):
response_words = response.split()
# build the total words which contains both search words and response words
total_words = self.search_words + response_words
self.compute_words_frequency(total_words)
self.word_measurer.load_synsets(response_words)
# load the POS tags of response words
response_POS_map = self.load_POS_map(response)
# build the similarity vector and order vector between search words and total words
search_vectors = self.build_sim_vectors(self.search_words, total_words, self.search_POS_map)
# build the similarity vector and order vector between response words and total words
response_vectors = self.build_sim_vectors(response_words, total_words, response_POS_map)
# compute the semantic similarity with similarity vector and order vector
sim = self.compute_similarity(search_vectors, response_vectors)
return sim
def __init__(self, brown_ic, search_query):
CompoundPhraseSimMeasurer.__init__(self, search_query)
self.brown_ic = brown_ic
self.search_POS_map = self.load_POS_map(search_query)
self.word_measurer = SemanticWordSimMeasurer(brown_ic, self.search_words)
