Title language model for information retrieval

Rong Jin, Alexander G. Hauptmann, ChengXiang Zhai

Abstract: In this paper, we propose a new language model, namely, a title language model, for information retrieval. Different from the traditional language model used for retrieval, we define the conditional probability P(Q|D) as the probability of using query Q as the title for document D. We adopted the statistical translation model learned from the title and document pairs in the collection to compute the probability P(Q|D). To avoid the sparse data problem, we propose two new smoothing methods. In the experiments with four different TREC document collections, the title language model for information retrieval with the new smoothing method outperforms both the traditional language model and the vector space model for IR significantly.