A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A hidden Markov model information retrieval system
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval as statistical translation
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Title language model for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Biterm language models for document retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Passage retrieval based on language models
Proceedings of the eleventh international conference on Information and knowledge management
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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 mutual information of the words from training corpus and then design the algorithm to get the triggered words of the query in order to fix down the topic of query more clearly. We introduce the relative parameters into the document language model to form the trigger language model based IR system. Experiments show that the performance of trigger language model based IR system has been improved greatly. The precision of trigger language model increased 12% and recall increased nearly 10.8% compared with Ponte language model method.