Topic Tracking Based on Keywords Dependency Profile
Topic tracking is an important task of Topic Detection and Tracking (TDT).Its purpose is to detect stories,from a stream of news,related to known topics.Each topic is known by its association with several sample stories that discuss it.In this paper,we propose a new method to build the keywords dependency profile (KDP) of each story and track topic basing on similarity between the profiles of topic and story.In this method,keywords of a story are selected by document summarization technology.The KDP is built by keywords co-occurrence frequency in the same sentences of the story.We demonstrate this profile can describe the core events in a story accurately.Experiments on the mandarin resource of TDT4 and TDT5 show topic tracking system basing on KDP improves the performance by 13.25％ on training dataset and 7.49％ on testing data.set comparing to baseline.
Topic Detection and Tracking topic tracking word co-occurrence keywords dependency profile
Wei Zheng Yu Zhang Yu Hong Jili Fan Ting Liu
School of Computer Science and Technology,Harbin Institute of Technology 150001 Harbin,China
国际会议
4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)
哈尔滨
英文
129-140
2008-01-16（万方平台首次上网日期，不代表论文的发表时间）