A Topic Language Model-based Chinese IR System
Junlin Zhang, Sun Le, Yufang Sun · Zhongwen xinxi xuebao · 2005
Exact estimation of the document language model is important to the performance of the language model based IR system. In this paper we proposed a topic-based approach to language modeling for ad-hoc Information Retrieval. An improved two-stage k-means clustering method is designed to deal with the document collection and the clustered results are regarded as the topic information contained in the collection. Through combing the aspect model and text clustering technology, we can derive a more accurate document language model for ad-hoc Information Retrieval. Experiments have shown that the performance of IR system has been improved greatly. Compared with Jelinek-Mercer language model IR system, Precision of the trigger language model based IR system increased almost 16.17% and recall of the system increased 9.64%.