基于语义域语言模型的中文话题关联检测  

Chinese Topic Link Detection Based on Semantic Domain Language Model

作　　者：洪宇[1] 张宇[1] 范基礼[1] 刘挺[1] 李生[1] 

机构地区：[1]哈尔滨工业大学计算机科学与技术学院信息检索研究室,黑龙江哈尔滨150001

出　　处：《软件学报》2008年第9期2265-2275,共11页Journal of Software

基　　金：国家自然科学基金;国家高技术研究发展计划(863)~~

摘　　要：关联检测是话题检测与跟踪领域的基础性研究,其任务是检测任意新闻报道对是否论述同一话题.通过分析报道内容的结构关系和语义的分布规律,提出基于语义域语言模型的关联性检测方法,并在此基础上检验融入依存分析的语义描述策略对该模型性能的影响.实验采用TDT4中文语料进行评测,结果显示语义域语言模型显著改进了现有检测系统的性能,其最小DET代价降低了约3个百分点.Topic link detection is a foundational research in the field of topic detection and tracking, which detects whether two random stories talk about the same topic. This paper proposes a method of applying semantic domain language model to link detection, based on the structure relation among contents and the semantic distribution in a story, and also verifies the influence of the strategy incorporating dependency parsing into semantic description. Evaluation on Chinese Corpus of TDT4 show that the semantic domain language model substantially improved the performance of current detection system, whose minimum DET cost is reduced by about 3 percent.

关 键 词：关联检测 话题检测与跟踪 语义域 语言模型 依存分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]