Title language model for information retrieval

Rong Jin, Alex G. Hauptmann, Cheng Xiang Zhai

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)42-48
Number of pages7
JournalSIGIR Forum (ACM Special Interest Group on Information Retrieval)
DOIs
StatePublished - Jan 1 2002
Externally publishedYes
EventProceedings of the Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Tampere, Finland
Duration: Aug 11 2002Aug 15 2002

Keywords

  • Machine learning
  • Smoothing
  • Statistical translation model
  • Title language model

ASJC Scopus subject areas

  • Management Information Systems
  • Hardware and Architecture

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