An Improved Language Model-based Chinese IR System
QU Wei-min · Zhongwen xinxi xuebao · 2004
Language model based IR system proposed in recent 5 years has introduced the language model approach in the speech recognition area into the IR community and improves the performance of the IR system effectively. However, the assumption that all the indexed words are irrelative behind the method is not the truth. Though statistical MT approach alleviates the situation by taking the synonymy factor into account, it never helps to judge the different meanings of the same word in varied context. In this paper we propose the trigger language model based IR system to resolve the problem. Firstly we compute the association ratio of the words from training corpus and then get the triggered words collection of the query words to find the real meaning of the word in specific text context. We introduce the relative parameters into the document language model to form the trigger language model based IR system. Experiments have shown that the performance of trigger language model based IR system has been improved greatly. Compared with classical language model IR system, Precision of the trigger language model based IR system increased almost 12% and recall of the system increased 10.8%.