TTP: a Topic Time Parser on Chinese News from Internet
摘要
The topic-related temporal information reflects the topic feature on temporal dimensionality under the news text,so the topic time can be employed to establish and analyze the news topic model in several news-oriented tasks for web information processing,as well as be treated as the clue to the topic evolution tracking.Aiming to solve the problem that the news topic relates with the topic time weakly in the research on news information processing,a novel relation model on the news topic and the temporal information is constructed through analyzing features of news categories and the web structure in depth.According to the model,the extracting algorithm,which are based on weights comparison and unsupervised learning,for topic time are proposed.Meanwhile a topic time parser on Chinese news pages is implemented,which can extract and normalize topic time automatically.Experimental results show that the proposed algorithm contrasted to the similar methods has the higher precision,as well as improves the relativity between news topic and topic time.In addition the integrated system achieves the promising performance evaluation.