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...') 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']