ADD TEMPORAL INFORMATION TO DEPENDENCY STRUCTURE LANGUAGE MODEL FOR TOPIC DETECTION AND TRACKING
The dependency structure language model was proposed to overcome the limitation of unigram and bigram models in topic detection and tracking (TDT). But its structure is based on mathematical models, which may has problems to express information. In this paper a new approach of topic tracking of Chinese news articles is presented which improves the existing ones with temporal information. The technique is implemented in a framework of dependency structure language model (DSLM). The experiments show remarkable improvement to existing approaches.
Topic tracking Dependency structure language model Temporal information eztraction
JING QIU LE-JIAN LIAO
Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
1575-1580
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）