def get_description(self, query):
additional_words = []
synset = self._query_babelnet(query)
if synset is not None:
if len(synset) > 0:
if len(synset['glosses']) > 0:
if 'gloss' in synset['glosses'][0]:
sentences = synset['glosses'][0]['gloss'].split('.')
print('Description Result:')
print(sentences)
if sentences is not None:
print('found information for query: ' + query)
for i in range(
0,
min(len(sentences),
self._NUMBER_OF_SENTENCES)):
transformer = StringTransformer()
additional_sentence = transformer.transform(
sentences[i]).get_words_list()
additional_words.extend(additional_sentence)
else:
print('information not found for query: ' + query)
else:
print('information not found for query: ' + query)
else:
print('information not found for query: ' + query)
else:
print('information not found for query: ' + query)
return additional_words
def find(self, query):
transformer = StringTransformer()
analyzer = EnglishAnalyzer(Version.LUCENE_CURRENT)
reader = IndexReader.open(SimpleFSDirectory(File("index/")))
searcher = IndexSearcher(reader)
searcher.setSimilarity(BM25Similarity())
processed_query = ' '.join(
self._preprocessor(transformer.transform(query)))
query = QueryParser(Version.LUCENE_CURRENT, "content",
analyzer).parse(processed_query)
hits = searcher.get_description(query, 10)
result_list = []
for hit in hits.scoreDocs:
doc = searcher.doc(hit.doc)
result_list.append(doc.get("path").encode("utf-8"))
return result_list
def get_type(self, query):
additional_words = []
topic = self._query_freebase(query)
if topic is not None:
if '/common/topic/notable_types' in topic['property']:
sentences = topic['property']['/common/topic/notable_types']['values'][0]['text']
if sentences is not None:
print('found parent type for query: ' + query)
transformer = StringTransformer()
additional_sentence = transformer.transform(sentences).get_words_list()
additional_words.extend(additional_sentence)
else:
print('parent type not found for query: ' + query)
else:
print('parent type not found for query: ' + query)
else:
print('parent type not found for query: ' + query)
def find(self, query):
query = StringTransformer().transform(query)
query_array = self._vectorizer.transform([self._query_transformer.transform(query).get_words_str()]).toarray()
result = self._kdtree_index.query(query_array, k=len(self._service_array), return_distance=False)
result_list = []
for index in result[0]:
result_list.append(self._service_array[index])
return result_list
def find(self, query):
query = StringTransformer().transform(query)
query_vector = self._dictionary.doc2bow(self._preprocessor(self._query_transformer.transform(query).get_words_list()))
query_tfidf_vector = self._tfidf_model[query_vector]
results = self._index[query_tfidf_vector]
results = sorted(enumerate(results), key=lambda item: -item[1])
result_list = []
for tuple_result in results:
result_list.append(self._service_array[tuple_result[0]])
return result_list
def find(self, query):
query = StringTransformer().transform(query)
query_array = self._vectorizer.transform(
[self._query_transformer.transform(query).get_words_str()])
query_array = self._svd.transform(query_array.toarray())
result = self._lsi_index.kneighbors(query_array,
return_distance=False)[0]
result_list = []
for index in result:
result_list.append(self._service_array[index])
return result_list
def find(self, query):
transformer = StringTransformer()
query = self._preprocessor(
transformer.transform(query).get_words_list())
# Filter words from the query that aren't in the vocabulary
query = list([x for x in query if x in self._fasttext_model.vocab])
results = []
for key in list(self._service_map.keys()):
# Assign 0 similarty for empty documents, otherwise calculate similarity
if self._service_map[key]:
results.append((key,
self._fasttext_model.n_similarity(
query, self._service_map[key])))
else:
results.append((key, 0))
results = sorted(results, key=lambda item: -item[1])
result_list = []
for tuple_result in results:
result_list.append(tuple_result[0])
return result_list
def get_description(self, query):
additional_words = []
topic = self._query_freebase(query)
if topic is not None:
if '/common/topic/article' in topic['property']:
if '/common/document/text' in topic['property']['/common/topic/article']['values'][0]['property']:
sentences = topic['property']['/common/topic/article']['values'][0]['property']['/common/document/text']['values'][0]['value'].split('.')
if sentences is not None:
print('found information for query: ' + query)
for i in range(0, min(len(sentences), self._NUMBER_OF_SENTENCES)):
transformer = StringTransformer()
additional_sentence = transformer.transform(sentences[i]).get_words_list()
additional_words.extend(additional_sentence)
else:
print('information not found for query: ' + query)
else:
print('information not found for query: ' + query)
else:
print('information not found for query: ' + query)
return additional_words
def find(self, query):
query = StringTransformer().transform(query)
query_vector = self._vectorizer.transform(
[self._query_transformer.transform(query).get_words_str()])
query_vae = self._model.transform(query_vector)
results = cosine_similarity(query_vae, self._index)
results = sorted(enumerate(results[0]), key=lambda item: -item[1])
result_list = []
for tuple_result in results:
result_list.append(self._service_array[tuple_result[0]])
return result_list
def find(self, query):
query = StringTransformer().transform(query)
query_vector = self._doc_to_nbow(
self._query_transformer.transform(query).get_words_list())
query_vector = np.expand_dims(query_vector, axis=0)
query_vae = self._model.transform(query_vector)
results = cosine_similarity(query_vae, self._index)
results = sorted(enumerate(results[0]), key=lambda item: -item[1])
result_list = []
for tuple_result in results:
result_list.append(self._service_array[tuple_result[0]])
return result_list
def find(self, query):
query = StringTransformer().transform(query)
query_array = self._vectorizer.transform([self._query_transformer.transform(query).get_words_str()]).toarray()
target_label = self._cluster_index.predict(query_array)[0]
target_indexes = self._cluster[target_label].kneighbors(query_array, return_distance=False)[0]
result = []
for target in target_indexes:
result.append(self._document_cluster[target_label][target][1])
result_list = []
for index in result:
result_list.append(self._service_array[index])
return result_list
