def perform(self, input):
message = Serializer.parse(input, Message)
if message.type == 'command':
if message.body == 'DIE':
self.logger.info("Received command message DIE")
self.stop()
else:
self.logger.error('Unknown command message: %s', message.body)
self.deliver(message)
question = Question(message.body)
question.tokens = word_tokenize(question.body)
for snippet in question.snippets:
snippet.sentences = self.tokenize(snippet.text)
message.body = question
self.deliver(message)
def perform(self, input):
""""""
""" Kaichen Chen 0304 """
print "Splitter {}: received, start to perform.".format(time())
message = Serializer.parse(input, Message)
if message.type == 'command':
if message.body == 'DIE':
self.logger.info("Received command message DIE")
self.stop()
else:
self.logger.error('Unknown command message: %s', message.body)
self.deliver(message)
question = Question(message.body)
question.tokens = word_tokenize(question.body)
for snippet in question.snippets:
snippet.sentences = self.tokenize(snippet.text)
message.body = question
self.deliver(message)
}, {
u'offsetInBeginSection': 1276,
u'offsetInEndSection': 1608,
u'text':
u'The increased odds ratios with African Americans was retained in post-menopausal women, while the protective odds ratios for pregnancy, smoking and oral contraceptives (OCs) became stronger in pre-menopausal women. The pattern by duration and timing of use does not suggest an etiologic role for OCs or hormone replacement therapy. ',
u'beginSection': u'abstract',
u'document': u'http://www.ncbi.nlm.nih.gov/pubmed/16570277',
u'endSection': u'abstract'
}, {
u'offsetInBeginSection': 1044,
u'offsetInEndSection': 1221,
u'text':
u'The use of hormone replacement therapy in symptomatic postmenopausal women either with previously treated disease or with dormant tumors is discussed, but remains controversial.',
u'beginSection': u'abstract',
u'document': u'http://www.ncbi.nlm.nih.gov/pubmed/15006250',
u'endSection': u'abstract'
}]
}
question = Question(data)
question.tokens = word_tokenize(question.body)
for snippet in question.snippets:
snippet.sentences = tokenize(snippet.text)
sci = CoreMMR()
sentences = sci.getRankedList(question)
print sentences
print 'Q. ' + question.body
for i, s in enumerate(sentences):
print "{} {}".format(i, s)
