def __init__(self, name, entity_link_min_score=0.3, topn=50, use_search_cache=True, use_answers_cache=True, **kwargs):
Ranker.__init__(self, name, **kwargs)
answers_cache_file = globals.config.get('WebSearchAnswers', 'websearch-answers-cache')
self._answers_cache = shelve.open(answers_cache_file) if use_answers_cache else dict()
self._searcher = BingWebSearchApi(globals.config.get('WebSearchAnswers', 'bing-api-key'), use_search_cache)
self._topn = topn
self._entity_linking_score_threshold = entity_link_min_score
self.parameters.web_search_candidates = True
def __init__(self, name, **kwargs):
Ranker.__init__(self, name, **kwargs)
answers_file = globals.config.get('WebSearchAnswers', 'websearch-answers')
self.answers = dict()
def object_decoder(q):
rank = int(q['id'].split("-")[1])
return q['utterance'], q['result'], rank
self.answers = dict()
for question, answer, rank in json.load(open(answers_file, 'r'), object_hook=object_decoder, encoding='utf-8'):
if question not in self.answers:
self.answers[question] = []
self.answers[question].append((answer, rank))
import operator
for question, answers in self.answers.iteritems():
self.answers[question] = [answer for answer, rank in sorted(answers, key=operator.itemgetter(1))]
def __init__(self, name, topn=50, **kwargs):
Ranker.__init__(self, name, **kwargs)
answers_cache_file = globals.config.get('WebSearchAnswers', 'sentsearch-answers-cache')
self._answers_cache = shelve.open(answers_cache_file)
self._searcher = SentSearchApi()
self._topn = topn