def generate_document_vectors(question_text, question_tokens2pos, questions_search_results):
if not _documents_vectors_cache:
import os
cache_file = globals.config.get('WebSearchFeatures', 'document-vectors')
if os.path.isfile(cache_file):
logger.info("Reading cached document vectors...")
with open(cache_file, 'r') as inp:
# Unpickle until the end of file is reached
index = 0
while True:
try:
question, vectors = pickle.load(inp)
_documents_vectors_cache[question] = vectors
except (EOFError, pickle.UnpicklingError):
break
index += 1
if index % 1000 == 0:
logger.info("Read " + str(index) + " document vectors...")
logger.info("Reading cached document vectors done!")
if question_text in _documents_vectors_cache:
return _documents_vectors_cache[question_text]
documents_vectors = []
snippets_vectors = []
fragment_vectors = []
combined_doc_token2pos = dict()
combined_doc_entity2pos = dict()
combined_doc_snippet_token2pos = dict()
combined_doc_snippet_entity2pos = dict()
for document in questions_search_results[question_text][:globals.SEARCH_RESULTS_TOPN]:
# Whole document
doc_entity2pos = document.get_mentioned_entities_to_pos()
doc_token2pos, doc_lemma2pos = document.get_token_to_positions_map()
documents_vectors.append({
"entity": SparseVector.from_2pos(doc_entity2pos),
"entity_tfidf": SparseVector.from_2pos(doc_entity2pos,
element_calc_func=SparseVector.compute_tfidf_entity_elements),
"token": SparseVector.from_2pos(doc_token2pos),
"token_tfidf": SparseVector.from_2pos(doc_token2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements),
})
merge_2pos_dicts(combined_doc_token2pos, doc_token2pos)
merge_2pos_dicts(combined_doc_entity2pos, doc_entity2pos)
# Snippet
doc_snippet_entity2pos = document.get_snippet_entities_to_pos()
doc_snippet_token2pos, doc_snippet_lemma2pos = document.get_snippet_token2pos()
snippets_vectors.append({
"entity": SparseVector.from_2pos(doc_snippet_entity2pos),
"entity_tfidf": SparseVector.from_2pos(doc_snippet_entity2pos,
element_calc_func=SparseVector.compute_tfidf_entity_elements),
"token": SparseVector.from_2pos(doc_snippet_token2pos),
"token_tfidf": SparseVector.from_2pos(doc_snippet_token2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements),
})
merge_2pos_dicts(combined_doc_snippet_token2pos, doc_snippet_token2pos)
merge_2pos_dicts(combined_doc_snippet_entity2pos, doc_snippet_entity2pos)
# Best fragment
fragment_token2pos, fragment_entity2pos = \
WebSearchResult.get_best_fragment_positions(doc_token2pos, doc_entity2pos, question_tokens2pos)
fragment_vectors.append({
"entity": SparseVector.from_2pos(fragment_entity2pos),
"entity_tfidf": SparseVector.from_2pos(fragment_entity2pos,
element_calc_func=SparseVector.compute_tfidf_entity_elements),
"token": SparseVector.from_2pos(fragment_token2pos),
"token_tfidf": SparseVector.from_2pos(fragment_token2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements),
})
combined_documents_vector = {
"entity": SparseVector.from_2pos(combined_doc_entity2pos),
"entity_tfidf": SparseVector.from_2pos(combined_doc_entity2pos,
element_calc_func=SparseVector.compute_tfidf_entity_elements),
"token": SparseVector.from_2pos(combined_doc_token2pos),
"token_tfidf": SparseVector.from_2pos(combined_doc_token2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements),
}
combined_document_snippets_vector = {
"entity": SparseVector.from_2pos(combined_doc_snippet_entity2pos),
"entity_tfidf": SparseVector.from_2pos(combined_doc_snippet_entity2pos,
element_calc_func=SparseVector.compute_tfidf_entity_elements),
"token": SparseVector.from_2pos(combined_doc_snippet_token2pos),
"token_tfidf": SparseVector.from_2pos(combined_doc_snippet_token2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements),
}
# Cache the computed vectors.
_documents_vectors_cache[question_text] = (documents_vectors, snippets_vectors, fragment_vectors,
combined_documents_vector, combined_document_snippets_vector)
return documents_vectors, snippets_vectors, fragment_vectors, combined_documents_vector, combined_document_snippets_vector
def generate_text_based_features(candidate):
# Get candidate answers
answers = map(unicode.lower, candidate.get_results_text())
# Skip empty and extra-long answers.
if len(answers) == 0:
return dict()
# Get answers descriptions.
answers_descriptions = ['\n'.join(KBEntity.get_entity_descriptions_by_name(answer, keep_most_triples_only=True))
for answer in answers]
# Get question text.
question_text = candidate.query.original_query
question_tokens2pos = dict((token, [1, ]) for token in tokenize(question_text))
question_token_tfidf = SparseVector.from_2pos(question_tokens2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements)
# Get question entities
question_entities2pos = dict((entity.entity.name.lower(), [1, ]) for entity in candidate.matched_entities)
question_entitytoken2pos = dict((token, [1, ])
for entity in candidate.matched_entities
for token in tokenize(entity.entity.name))
question_entity_tfidf = SparseVector.from_2pos(question_entitytoken2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements)
# Get search results and check that they aren't empty
questions_search_results = get_questions_serps()
documents_vectors = []
snippets_vectors = []
fragment_vectors = []
combined_documents_vector = dict()
combined_document_snippets_vector = dict()
representations = ["entity_tfidf",
"token_tfidf",
# "entity",
# "token",
]
for r in representations:
combined_documents_vector[r] = dict()
combined_document_snippets_vector[r] = dict()
if question_text not in questions_search_results:
logger.warning("No search results found for the question %s" % question_text)
else:
documents_vectors, snippets_vectors, fragment_vectors, combined_documents_vector,\
combined_document_snippets_vector = generate_document_vectors(question_text,
question_tokens2pos,
questions_search_results)
answer_entity2pos = dict((answer_entity, [1, ]) for answer_entity in answers)
answer_token2pos = dict((answer_token, [1, ]) for answer_entity in answers
for answer_token in tokenize(answer_entity))
answers_vectors = {
"token_tfidf": SparseVector.from_2pos(answer_token2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements),
"entity_tfidf": SparseVector.from_2pos(answer_entity2pos,
element_calc_func=SparseVector.compute_tfidf_entity_elements),
# "entity": SparseVector.from_2pos(answer_entity2pos),
# "token": SparseVector.from_2pos(answer_token2pos),
}
answer_descriptions_token2pos = dict((token, [1, ]) for description in answers_descriptions
for token in tokenize(description))
answer_description_vectors = {
"token_tfidf": SparseVector.from_2pos(answer_descriptions_token2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements),
"entity_tfidf": SparseVector(dict()),
# Keeping only tf-idf similarities. This seems to be enough.
# "token": SparseVector.from_2pos(answer_descriptions_token2pos),
# "entity": SparseVector(dict()),
}
similarity_functions = [
("cosine", Similarity.cosine_similarity),
# ("itersection", Similarity.intersection_similarity),
# ("normalized_intersection", Similarity.normalized_intersection_similarity),
# ("bm25", Similarity.bm25_similarity),
]
features = dict()
for similarity_name, similarity in similarity_functions:
# Computing document-answer similarities for each representation.
document_answer_similarities = {}
for representation in representations:
if representation not in document_answer_similarities:
document_answer_similarities[representation] = []
for doc_vector in documents_vectors:
document_answer_similarities[representation].append(similarity(representation,
doc_vector[representation],
answers_vectors[representation]))
for representation in representations:
features.update({
"text_features:avg_document_answer_%s_%s" % (representation, similarity_name):
avg(document_answer_similarities[representation]),
"text_features:max_document_answer_%s_%s" % (representation, similarity_name):
max(document_answer_similarities[representation]) if document_answer_similarities[representation]
else 0.0,
})
# logger.info("Snippet-answer similarity...")
# Computing snippet-answer similarities for each representation.
snippet_answer_similarities = {}
for representation in representations:
if representation not in snippet_answer_similarities:
snippet_answer_similarities[representation] = []
for snippet_vector in snippets_vectors:
snippet_answer_similarities[representation].append(similarity(representation,
snippet_vector[representation],
answers_vectors[representation]))
for representation in representations:
features.update({
"text_features:avg_snippet_answer_%s_%s" % (representation, similarity_name):
avg(snippet_answer_similarities[representation]),
"text_features:max_snippet_answer_%s_%s" % (representation, similarity_name):
max(snippet_answer_similarities[representation]) if snippet_answer_similarities[representation] else 0.0,
})
# logger.info("Fragment-answer similarity...")
# Best BM25 fragment-answer similarities.
# Weren't very efficient and therefore I remove this features. There is a chance that there is a bug in the features.
# fragment_answer_similarities = {}
# for fragment_vector in fragment_vectors:
# for representation in representations:
# if representation not in fragment_answer_similarities:
# fragment_answer_similarities[representation] = []
# fragment_answer_similarities[representation].append(similarity(representation,
# fragment_vector[representation],
# answers_vectors[representation]))
#
# for representation in representations:
# features.update({
# "text_features:avg_fragment_answer_%s_%s" % (representation, similarity_name):
# avg(fragment_answer_similarities[representation]),
# "text_features:max_fragment_answer_%s_%s" % (representation, similarity_name):
# max(fragment_answer_similarities[representation]) if fragment_answer_similarities[representation] else 0.0,
# })
# logger.info("Combined document-answer similarity...")
# Combined documents answer similarity
for representation in representations:
combineddoc_answer_similarity = similarity(representation,
combined_documents_vector[representation],
answers_vectors[representation])
features.update({
"text_features:combdocument_answer_%s_%s" % (representation, similarity_name):
combineddoc_answer_similarity,
})
# logger.info("Combined snippet-answer similarity...")
for representation in representations:
combineddocsnippet_answer_similarity = similarity(representation,
combined_document_snippets_vector[representation],
answers_vectors[representation])
features.update({
"text_features:combdocument_snippet_answer_%s_%s" % (representation, similarity_name):
combineddocsnippet_answer_similarity,
})
# logger.info("Description-question similarity...")
# These features aren't very efficient either. The next candidate for removal.
description_question_entity_similarity = similarity("token_tfidf", question_entity_tfidf,
answer_description_vectors["token_tfidf"])
description_question_token_similarity = similarity("token_tfidf", question_token_tfidf,
answer_description_vectors["token_tfidf"])
features.update({
"text_features:description_question_entitytoken_%s" % similarity_name:
description_question_entity_similarity,
"text_features:description_question_token_%s" % similarity_name: description_question_token_similarity,
})
# Description - question embedding similarity.
description_question_token_embedding_avg_similarity = Similarity.embedding_avg_idf_similarity(
"token_tfidf", question_token_tfidf, answer_description_vectors["token_tfidf"])
description_question_token_embedding_n_similarity = Similarity.embedding_avg_idf_similarity(
"token_tfidf", question_token_tfidf, answer_description_vectors["token_tfidf"])
features.update({
"text_features:description_question_token_avg_idf_embeddings":
description_question_token_embedding_avg_similarity,
"text_features:description_question_token_n_embeddings":
description_question_token_embedding_n_similarity,
})
# Remove features with 0 score.
features = dict((feature, value) for feature, value in features.iteritems() if value != 0.0)
return features
