def run(self):
logging.info('running QA...')
input_file = self.cfg.get('qa', 'input_file')
for entry in QAParser.parse_file(input_file):
logging.info('processing text...')
all_text = "\n".join([doc['text'] for doc in entry['docs']])
model = self.text_to_4lang.process(
all_text, dep_dir=self.dep_dir, fn='text')
print_text_graph(model, self.graph_dir)
model_graph = MachineGraph.create_from_machines(model.values())
for question in entry['questions']:
answer = self.answer_question(question, model, model_graph)
print answer['text']
def run(self):
logging.info('running QA...')
input_file = self.cfg.get('qa', 'input_file')
for entry in QAParser.parse_file(input_file):
logging.info('processing text...')
sens = []
for doc in entry['docs']:
sens += self.sent_detector.tokenize(doc['text'])
model = self.text_to_4lang.process(sens)
for question in entry['questions']:
answer = self.answer_question(question, model)
print answer
def run(self):
logging.info('running QA...')
input_file = self.cfg.get('qa', 'input_file')
for entry in QAParser.parse_file(input_file):
logging.info('processing text...')
sens = []
for doc in entry['docs']:
sens += self.sent_detector.tokenize(doc['text'])
model = self.text_to_4lang.process(sens)
for question in entry['questions']:
answer = self.answer_question(question, model)
print answer
def run(self):
logging.info('running QA...')
input_file = self.cfg.get('qa', 'input_file')
for entry in QAParser.parse_file(input_file):
logging.info('processing text...')
all_text = "\n".join([doc['text'] for doc in entry['docs']])
model = self.text_to_4lang.process(all_text,
dep_dir=self.dep_dir,
fn='text')
print_text_graph(model, self.graph_dir)
model_graph = MachineGraph.create_from_machines(model.values())
for question in entry['questions']:
answer = self.answer_question(question, model, model_graph)
print answer['text']
