def _other_recognition(self, tagged_sentences, all_entities, question):
# Nouns retrieval
nouns = []
for sentence in tagged_sentences:
nouns += filter(lambda x: x[1] == "NN", sentence)
nouns = [noun for (noun, tag) in nouns]
# Nouns filtering
# Remove all entities that are nouns
all_entities = set(itertools.chain(*map(str.split, all_entities)))
nouns = [noun for noun in nouns if noun not in all_entities]
features = QuestionClassifier.get_features(question.text, "hn")
head = features["head"]
if head == "":
return nouns
# Filter nouns with WordNet synsets
try:
threshold = float(
MyConfig.get("answer_extraction", "other_threshold"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
threshold = 0.6
try:
ic = wordnet_ic.ic(MyConfig.get("answer_extraction", "ic"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
ic = wordnet_ic.ic("ic-bnc.dat")
result = []
head_synsets = wn.synsets(head, pos=wn.NOUN)
if len(head_synsets) == 0:
noun_synsets = wn.synsets(features["noun"], pos=wn.NOUN)
if len(noun_synsets) == 0:
return nouns
else:
head_synset = noun_synsets[0]
else:
head_synset = head_synsets[0]
for noun in nouns:
try:
noun_synset = wn.synsets(noun, pos=wn.NOUN)[0]
if threshold < noun_synset.lin_similarity(head_synset,
ic) < 0.9:
result.append(noun)
except IndexError:
continue
return result
def _stanford_ner(self, text, searched_entity, question):
sentences = nltk.sent_tokenize(text)
tokenized_sentences = [nltk.word_tokenize(s) for s in sentences]
tagged_sentences = [nltk.pos_tag(s) for s in tokenized_sentences]
# Entity Classification
try:
host = MyConfig.get("answer_extraction", "stanford_host")
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
host = "localhost"
try:
port = int(MyConfig.get("answer_extraction", "stanford_port"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
port = 1234
try:
recognizer = StanfordNER.get_instance(host, port)
text = recognizer.process(text)
except StanfordNERError:
logger = logging.getLogger("qa_logger")
logger.warning("Stanford NER not available, using NLTK NER")
return self._nltk_ner(text, searched_entity)
# XML Parsing
text = "<xml>" + text.replace("&", "") + "</xml>"
try:
tree = fromstring(text)
except:
return []
# Entity Extraction
entities = []
all_entities = []
for element in tree.iterchildren():
word = "" if element.text is None else element.text
if element is None:
continue
if element.tag == searched_entity:
entities.append(word)
all_entities.append(word)
if 'OTHER' == searched_entity:
entities += self._other_recognition(tagged_sentences, all_entities,
question)
if 'NUMBER' == searched_entity:
entities += self._number_recognition(text, tagged_sentences,
all_entities)
return entities
def _stanford_ner(self, text, searched_entity, question):
sentences = nltk.sent_tokenize(text)
tokenized_sentences = [nltk.word_tokenize(s) for s in sentences]
tagged_sentences = [nltk.pos_tag(s) for s in tokenized_sentences]
# Entity Classification
try:
host = MyConfig.get("answer_extraction", "stanford_host")
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
host = "localhost"
try:
port = int(MyConfig.get("answer_extraction", "stanford_port"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
port = 1234
try:
recognizer = StanfordNER.get_instance(host, port)
text = recognizer.process(text)
except StanfordNERError:
logger = logging.getLogger("qa_logger")
logger.warning("Stanford NER not available, using NLTK NER")
return self._nltk_ner(text, searched_entity)
# XML Parsing
text = "<xml>" + text.replace("&", "") + "</xml>"
try:
tree = fromstring(text)
except:
return []
# Entity Extraction
entities = []
all_entities = []
for element in tree.iterchildren():
word = "" if element.text is None else element.text
if element is None:
continue
if element.tag == searched_entity:
entities.append(word)
all_entities.append(word)
if 'OTHER' == searched_entity:
entities += self._other_recognition(tagged_sentences, all_entities, question)
if 'NUMBER' == searched_entity:
entities += self._number_recognition(text, tagged_sentences, all_entities)
return entities
def _other_recognition(self, tagged_sentences, all_entities, question):
# Nouns retrieval
nouns = []
for sentence in tagged_sentences:
nouns += filter(lambda x: x[1] == "NN", sentence)
nouns = [noun for (noun, tag) in nouns]
# Nouns filtering
# Remove all entities that are nouns
all_entities = set(itertools.chain(*map(str.split, all_entities)))
nouns = [noun for noun in nouns if noun not in all_entities]
features = QuestionClassifier.get_features(question.text, "hn")
head = features["head"]
if head == "":
return nouns
# Filter nouns with WordNet synsets
try:
threshold = float(MyConfig.get("answer_extraction", "other_threshold"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
threshold = 0.6
try:
ic = wordnet_ic.ic(MyConfig.get("answer_extraction", "ic"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
ic = wordnet_ic.ic("ic-bnc.dat")
result = []
head_synsets = wn.synsets(head, pos=wn.NOUN)
if len(head_synsets) == 0:
noun_synsets = wn.synsets(features["noun"], pos=wn.NOUN)
if len(noun_synsets) == 0:
return nouns
else:
head_synset = noun_synsets[0]
else:
head_synset = head_synsets[0]
for noun in nouns:
try:
noun_synset = wn.synsets(noun, pos=wn.NOUN)[0]
if threshold < noun_synset.lin_similarity(head_synset, ic) < 0.9:
result.append(noun)
except IndexError:
continue
return result
def search(self):
logger = logging.getLogger("qa_logger")
logger.info("%s:\tDocument Retrieval", self.id_q)
search_engines = self._get_search_engines()
try:
num = int(MyConfig.get("document_retrieval", "n_results"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
num = 10
results = []
for engine in search_engines:
try:
results += engine.search(self.query, count=num)
except Exception as e:
logger = logging.getLogger("qa_logger")
logger.error("Problem with search engine.")
logger.debug(e)
sys.exit(1)
doc_list = []
# rank loops over [0..num-1]
rank = 0
# ignore repeated urls
unique_urls = set()
for resource in results:
if resource.url in unique_urls:
continue
unique_urls.add(resource.url)
# rank+1 loops over [1..num]
# rank+1 is the relative position of the results
doc_list.append(Document(resource, rank + 1))
rank = (rank + 1) % num
try:
if MyConfig.get("persistence", "document") == "True":
output = open("documentos.pkl", "wb")
pickle.dump(doc_list, output, 0)
output.close()
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
return doc_list
def search(self):
logger = logging.getLogger("qa_logger")
logger.info("%s:\tDocument Retrieval", self.id_q)
search_engines = self._get_search_engines()
try:
num = int(MyConfig.get("document_retrieval", "n_results"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
num = 10
results = []
for engine in search_engines:
try:
results += engine.search(self.query, count=num)
except Exception as e:
logger = logging.getLogger("qa_logger")
logger.error("Problem with search engine.")
logger.debug(e)
sys.exit(1)
doc_list = []
# rank loops over [0..num-1]
rank = 0
# ignore repeated urls
unique_urls = set()
for resource in results:
if resource.url in unique_urls:
continue
unique_urls.add(resource.url)
# rank+1 loops over [1..num]
# rank+1 is the relative position of the results
doc_list.append(Document(resource, rank + 1))
rank = (rank + 1) % num
try:
if MyConfig.get("persistence", "document") == "True":
output = open("documentos.pkl", "wb")
pickle.dump(doc_list, output, 0)
output.close()
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
return doc_list
def process_answer(self, passage, question):
q = question.text
p = passage.text
searched_entity = self._question_classification(q)
try:
ner_algorithm = MyConfig.get("answer_extraction", "ner")
except MyConfigException as e:
ner_algorithm = "stanford"
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
if ner_algorithm == "nltk":
entities = self._nltk_ner(p, searched_entity, question)
else:
entities = self._stanford_ner(p, searched_entity, question)
entities = self._filter_entities(entities, q)
exact, window, score = self._entity_ranking(entities)
answer = Answer(passage, question, window, exact, score)
return answer
def split_into_passages(self, document):
if document is None or document.content is None or document.content == "":
return []
lines = document.content.split("\n")
passage_list = []
try:
n_lines = int(MyConfig.get("document_segmentation", "n_lines"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
n_lines = 5
# Iterating over the lines of the document
# obtaining overlapped passages:
# max(1, len(lines)-n_lines+1)
# Don't ask: magic numbers ;-)
for i in range(0, max(1, len(lines) - n_lines + 1)):
lines_of_text = lines[i:i + n_lines]
# Join list of lines
piece_of_text = "\n".join(lines_of_text)
passage_list.append(Passage(piece_of_text, document))
# Adds search engine snippet
passage_list.append(Passage(document.description, document))
return passage_list
def __init__(self, result, rank):
self.title = result.title
self.url = utils.from_unicode_to_ascii(result.url)
self.rank = rank
self.description = utils.from_unicode_to_ascii(result.description)
self.content = utils.from_unicode_to_ascii(self._get_content(result))
# Split document into passages
try:
algorithm = MyConfig.get("document_segmentation", "algorithm")
if algorithm == "lines":
self.passages = SplitIntoLinesAlgorithm.split_into_passages(
self)
elif algorithm == "paragraphs":
self.passages = SplitIntoParagraphsAlgorithm.split_into_passages(
self)
elif algorithm == "sentences":
self.passages = SplitIntoSentencesAlgorithm.split_into_passages(
self)
else:
self.passages = SplitIntoParagraphsAlgorithm.split_into_passages(
self)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
self.passages = SplitIntoParagraphsAlgorithm.split_into_passages(
self)
def process_answer(self, passage, question):
q = question.text
p = passage.text
searched_entity = self._question_classification(q)
try:
ner_algorithm = MyConfig.get("answer_extraction", "ner")
except MyConfigException as e:
ner_algorithm = "stanford"
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
if ner_algorithm == "nltk":
entities = self._nltk_ner(p, searched_entity, question)
else:
entities = self._stanford_ner(p, searched_entity, question)
entities = self._filter_entities(entities, q)
exact, window, score = self._entity_ranking(entities)
answer = Answer(passage, question, window, exact, score)
return answer
def get_run_tag(self):
try:
exact = MyConfig.get("show_answer", "exact") == "True"
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
exact = False
return ("plna" + ("ex" if exact else "st") + "031ms", exact)
def get_run_tag(self):
try:
exact = MyConfig.get("show_answer", "exact") == "True"
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
exact = False
return ("plna" + ("ex" if exact else "st") + "031ms", exact)
def _question_classification(self, question):
# Choose the specified classifier
try:
features = MyConfig.get("answer_extraction", "question_features")
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
features = "fnh"
try:
classifier_file = MyConfig.get("answer_extraction", "question_classifier")
classifier_path = os.path.join("qc", features, classifier_file)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
classifier_path = os.path.join("qc", "fhn", "qc_bayes.pkl")
# Question classification
return QuestionClassifier.classify(classifier_path, question, features)
def _get_search_engines(self):
try:
lang = MyConfig.get("document_retrieval", "lang")
engines = safe_eval(MyConfig.get("document_retrieval", "engines"))
throttle = MyConfig.get("document_retrieval", "throttle")
l = []
for (engine, license) in engines:
# Eval to something like this:
# Google(google_license, throttle, lang)
l.append(eval(engine + "(\"" + license + "\", " + throttle + ", " + lang + ")"))
return l
except MyConfigException:
sys.exit("_get_search_engines: config error")
except:
logger = logging.getLogger("qa_logger")
logger.exception("_get_search_engines: fatal error")
sys.exit(2)
def calculate_score(self, question, passage):
rank = passage.document.rank
q = question.text
text = passage.text
# Removestopwords from question and passage
# and split it into words
q = StopwordsAlgorithm.formulate_query(q).split()
text = StopwordsAlgorithm.formulate_query(text).split()
# Apply stemming to q and text
porter = PorterStemmer()
q = map(porter.stem, q)
text = map(porter.stem, text)
score = 0
searched_term = 0
last_match = 0
first_match = True
if len(q) < 1:
return 0
for i, word in enumerate(text):
if searched_term >= len(q):
searched_term = 0
first_match = True
if word == q[searched_term]:
if first_match:
score += 1
first_match = False
else:
score += 1.0 / (i - last_match)
last_match = i
searched_term += 1
try:
num = int(MyConfig.get("document_retrieval", "n_results"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
return score
# Reverse rank order from 1..n to n..1
rank = num - rank + 1.0
# Normalize rank from n..1 to 1..0.5
rank = (rank - 2 + num) / (2 * num - 2)
# Weight score by rank
score = score * rank
return score
def calculate_score(self, question, passage):
rank = passage.document.rank
q = question.text
text = passage.text
# Removestopwords from question and passage
# and split it into words
q = StopwordsAlgorithm.formulate_query(q).split()
text = StopwordsAlgorithm.formulate_query(text).split()
# Apply stemming to q and text
porter = PorterStemmer()
q = map(porter.stem, q)
text = map(porter.stem, text)
score = 0
searched_term = 0
last_match = 0
first_match = True
if len(q) < 1:
return 0
for i, word in enumerate(text):
if searched_term >= len(q):
searched_term = 0
first_match = True
if word == q[searched_term]:
if first_match:
score += 1
first_match = False
else:
score += 1.0 / (i - last_match)
last_match = i
searched_term += 1
try:
num = int(MyConfig.get("document_retrieval", "n_results"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
return score
# Reverse rank order from 1..n to n..1
rank = num - rank + 1.0
# Normalize rank from n..1 to 1..0.5
rank = (rank - 2 + num) / (2 * num - 2)
# Weight score by rank
score = score * rank
return score
def _question_classification(self, question):
# Choose the specified classifier
try:
features = MyConfig.get("answer_extraction", "question_features")
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
features = "fnh"
try:
classifier_file = MyConfig.get("answer_extraction",
"question_classifier")
classifier_path = os.path.join("qc", features, classifier_file)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
classifier_path = os.path.join("qc", "fhn", "qc_bayes.pkl")
# Question classification
return QuestionClassifier.classify(classifier_path, question, features)
def _get_search_engines(self):
try:
lang = MyConfig.get("document_retrieval", "lang")
engines = safe_eval(MyConfig.get("document_retrieval", "engines"))
throttle = MyConfig.get("document_retrieval", "throttle")
l = []
for (engine, license) in engines:
# Eval to something like this:
# Google(google_license, throttle, lang)
l.append(
eval(engine + "(\"" + license + "\", " + throttle + ", " +
lang + ")"))
return l
except MyConfigException:
sys.exit("_get_search_engines: config error")
except:
logger = logging.getLogger("qa_logger")
logger.exception("_get_search_engines: fatal error")
sys.exit(2)
def find_answer(self, question):
try:
algorithm = MyConfig.get("answer_extraction", "algorithm")
if (algorithm == "entity"):
self.answer = EntityRecognitionAlgorithm.process_answer(self, question)
else:
self.answer = EntityRecognitionAlgorithm.process_answer(self, question)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
self.answer = EntityRecognitionAlgorithm.process_answer(self, question)
return self.answer
def get_best_answers(self, passage_list, q):
logger = logging.getLogger("qa_logger")
logger.info("%s:\tAnswer Processing", q.id_q)
empty = passage_list == []
logger.info("%s:\t\tAnswer Extraction", q.id_q)
answer_list = []
for passage in passage_list:
a = passage.find_answer(q)
if a.is_successful():
answer_list.append(a)
if not answer_list:
return ([], empty)
logger.info("%s:\t\tAnswer Filtering", q.id_q)
# Obtain answer frequency
fd = FreqDist(answer_list)
# Normalize frequencies
normalize = fd.freq(fd.max())
# Modify scores by frequency
for answer in answer_list:
answer.score = int(answer.score * (fd.freq(answer) / normalize))
# Sort answers by score
answer_list.sort(key=lambda x: x.score, reverse=True)
# Filter bad answers
try:
threshold = int(MyConfig.get("answer_filtering", "threshold"))
except:
logger = logging.getLogger("qa_logger")
logger.error("answer quality threshold not found")
threshold = 50
answer_list = filter(lambda x: x.score > threshold, answer_list)
final_answers = []
for a in answer_list:
if a not in final_answers:
final_answers.append(a)
if len(final_answers) == 3:
break
return (final_answers, empty)
def get_best_answers(self, passage_list, q):
logger = logging.getLogger("qa_logger")
logger.info("%s:\tAnswer Processing", q.id_q)
empty = passage_list == []
logger.info("%s:\t\tAnswer Extraction", q.id_q)
answer_list = []
for passage in passage_list:
a = passage.find_answer(q)
if a.is_successful():
answer_list.append(a)
if not answer_list:
return ([], empty)
logger.info("%s:\t\tAnswer Filtering", q.id_q)
# Obtain answer frequency
fd = FreqDist(answer_list)
# Normalize frequencies
normalize = fd.freq(fd.max())
# Modify scores by frequency
for answer in answer_list:
answer.score = int(answer.score * (fd.freq(answer) / normalize))
# Sort answers by score
answer_list.sort(key=lambda x: x.score, reverse=True)
# Filter bad answers
try:
threshold = int(MyConfig.get("answer_filtering", "threshold"))
except:
logger = logging.getLogger("qa_logger")
logger.error("answer quality threshold not found")
threshold = 50
answer_list = filter(lambda x: x.score > threshold, answer_list)
final_answers = []
for a in answer_list:
if a not in final_answers:
final_answers.append(a)
if len(final_answers) == 3:
break
return (final_answers, empty)
def _formulate_query(self):
logger = logging.getLogger("qa_logger")
logger.info("%s:\tQuery Formulation", self.id_q)
try:
algorithm = MyConfig.get("query_formulation", "algorithm")
if algorithm == "stopwords":
return StopwordsAlgorithm.formulate_query(self.text)
else:
return StopwordsAlgorithm.formulate_query(self.text)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
return StopwordsAlgorithm.formulate_query(self.text)
def _formulate_query(self):
logger = logging.getLogger("qa_logger")
logger.info("%s:\tQuery Formulation", self.id_q)
try:
algorithm = MyConfig.get("query_formulation", "algorithm")
if algorithm == "stopwords":
return StopwordsAlgorithm.formulate_query(self.text)
else:
return StopwordsAlgorithm.formulate_query(self.text)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
return StopwordsAlgorithm.formulate_query(self.text)
def calculate_score(self, question):
try:
algorithm = MyConfig.get("passage_filtering", "algorithm")
if (algorithm == "similarity"):
self.score = SimilarityAlgorithm.calculate_score(question, self)
elif (algorithm == "proximity"):
self.score = ProximityAlgorithm.calculate_score(question, self)
elif (algorithm == "mixed"):
self.score = MixedAlgorithm.calculate_score(question, self)
else:
self.score = MixedAlgorithm.calculate_score(question, self)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
self.score = MixedAlgorithm.calculate_score(question, self)
def find_answer(self, question):
try:
algorithm = MyConfig.get("answer_extraction", "algorithm")
if (algorithm == "entity"):
self.answer = EntityRecognitionAlgorithm.process_answer(
self, question)
else:
self.answer = EntityRecognitionAlgorithm.process_answer(
self, question)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
self.answer = EntityRecognitionAlgorithm.process_answer(
self, question)
return self.answer
def calculate_score(self, question):
try:
algorithm = MyConfig.get("passage_filtering", "algorithm")
if (algorithm == "similarity"):
self.score = SimilarityAlgorithm.calculate_score(
question, self)
elif (algorithm == "proximity"):
self.score = ProximityAlgorithm.calculate_score(question, self)
elif (algorithm == "mixed"):
self.score = MixedAlgorithm.calculate_score(question, self)
else:
self.score = MixedAlgorithm.calculate_score(question, self)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
self.score = MixedAlgorithm.calculate_score(question, self)
def get_relevant_passages(self, doc_list, question):
logger = logging.getLogger("qa_logger")
logger.info("%s:\tPassage Retrieval", question.id_q)
logger.info("%s:\t\tDocument Segmentation", question.id_q)
passage_list = self.score_passages(doc_list, question)
passage_list.sort(key=lambda x: x.score, reverse=True)
# Select n best passages
try:
n = int(MyConfig.get("document_segmentation", "n_relevants"))
except:
n = 100
logger = logging.getLogger("qa_logger")
logger.warning("n_relevants not found")
return passage_list[:n]
def get_relevant_passages(self, doc_list, question):
logger = logging.getLogger("qa_logger")
logger.info("%s:\tPassage Retrieval", question.id_q)
logger.info("%s:\t\tDocument Segmentation", question.id_q)
passage_list = self.score_passages(doc_list, question)
passage_list.sort(key=lambda x: x.score, reverse=True)
# Select n best passages
try:
n = int(MyConfig.get("document_segmentation", "n_relevants"))
except:
n = 100
logger = logging.getLogger("qa_logger")
logger.warning("n_relevants not found")
return passage_list[:n]
def split_into_passages(self, document):
if document is None or document.content is None or document.content == "":
return []
try:
n_sentences = int(
MyConfig.get("document_segmentation", "n_sentences"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
n_sentences = 5
passage_list = self._aux(document.content, n_sentences, document)
# Adds search engine snippet
passage_list += self._aux(document.description, n_sentences, document)
return passage_list
def _get_content(self, result):
url = URL(result.url)
try:
timeout = int(MyConfig.get("document_retrieval", "timeout"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
timeout = 15
try:
mimetype = url.mimetype
content = utils.from_unicode_to_ascii(url.download(timeout=timeout, unicode=True))
except Exception as e:
# If we cannot retrieve the document, we skip it
logger = logging.getLogger("qa_logger")
logger.warning("%s couldn't be retrieved", result.url)
logger.warning(str(e))
return ""
return self._extract_text(content, mimetype)
def calculate_score(self, question, passage):
rank = passage.document.rank
q = question.text
text = passage.text
# Remove stopwords from question and passage
# and split it into words
q = StopwordsAlgorithm.formulate_query(q).split()
text = StopwordsAlgorithm.formulate_query(text).split()
# Apply stemming to q and text
porter = PorterStemmer()
q = map(porter.stem, q)
text = map(porter.stem, text)
# Filter all words in passage that they are
# not present in question
words = filter(lambda x: x in q, text)
# Our initial score is the number of coincidences
score = len(words)
try:
num = int(MyConfig.get("document_retrieval", "n_results"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
return score
# Reverse rank order from 1..n to n..1
rank = num - rank + 1.0
# Normalize rank from n..1 to 1..0.5
rank = (rank - 2 + num) / (2 * num - 2)
# Weight score by rank
score = score * rank
return score
def calculate_score(self, question, passage):
rank = passage.document.rank
q = question.text
text = passage.text
# Remove stopwords from question and passage
# and split it into words
q = StopwordsAlgorithm.formulate_query(q).split()
text = StopwordsAlgorithm.formulate_query(text).split()
# Apply stemming to q and text
porter = PorterStemmer()
q = map(porter.stem, q)
text = map(porter.stem, text)
# Filter all words in passage that they are
# not present in question
words = filter(lambda x: x in q, text)
# Our initial score is the number of coincidences
score = len(words)
try:
num = int(MyConfig.get("document_retrieval", "n_results"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
return score
# Reverse rank order from 1..n to n..1
rank = num - rank + 1.0
# Normalize rank from n..1 to 1..0.5
rank = (rank - 2 + num) / (2 * num - 2)
# Weight score by rank
score = score * rank
return score
def _get_content(self, result):
url = URL(result.url)
try:
timeout = int(MyConfig.get("document_retrieval", "timeout"))
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
timeout = 15
try:
mimetype = url.mimetype
content = utils.from_unicode_to_ascii(
url.download(timeout=timeout, unicode=True))
except Exception as e:
# If we cannot retrieve the document, we skip it
logger = logging.getLogger("qa_logger")
logger.warning("%s couldn't be retrieved", result.url)
logger.warning(str(e))
return ""
return self._extract_text(content, mimetype)
def __init__(self, result, rank):
self.title = result.title
self.url = utils.from_unicode_to_ascii(result.url)
self.rank = rank
self.description = utils.from_unicode_to_ascii(result.description)
self.content = utils.from_unicode_to_ascii(self._get_content(result))
# Split document into passages
try:
algorithm = MyConfig.get("document_segmentation", "algorithm")
if algorithm == "lines":
self.passages = SplitIntoLinesAlgorithm.split_into_passages(self)
elif algorithm == "paragraphs":
self.passages = SplitIntoParagraphsAlgorithm.split_into_passages(self)
elif algorithm == "sentences":
self.passages = SplitIntoSentencesAlgorithm.split_into_passages(self)
else:
self.passages = SplitIntoParagraphsAlgorithm.split_into_passages(self)
except MyConfigException as e:
logger = logging.getLogger("qa_logger")
logger.warning(str(e))
self.passages = SplitIntoParagraphsAlgorithm.split_into_passages(self)