Exploring Topic-based Language Models for Effective Web Information Retrieval

Rongmei Li, Rianne Kaptein, Djoerd Hiemstra, Jaap Kamps

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Abstract

The main obstacle for providing focused search is the relative opaqueness of search request -- searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can lead to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topical models with other language models based on cross-entropy. We first define our topical categories and train our topical models on the .GOV2 corpus by building parsimonious language models. We then test the topic-based model on TREC8 small Web data collection for ad-hoc search.Our experimental results show that the topic-based model outperforms the standard language model and parsimonious model.
Original languageEnglish
Title of host publicationProceedings of the Dutch-Belgian Information Retrieval Workshop (DIR 2008)
EditorsE. Hoenkamp, M. De Cock, V. Hoste
Place of PublicationEnschede
PublisherNeslia Paniculata
Pages65-71
Number of pages7
ISBN (Print)978-90-5681-282-9
Publication statusPublished - 14 Apr 2008
Event8th Dutch-Belgian Information Retrieval Workshop, DIR 2008 - Maastricht, Netherlands
Duration: 14 Apr 200815 Apr 2008
Conference number: 8

Conference

Conference8th Dutch-Belgian Information Retrieval Workshop, DIR 2008
Abbreviated titleDIR
CountryNetherlands
CityMaastricht
Period14/04/0815/04/08

Keywords

  • DB-IR: INFORMATION RETRIEVAL
  • IR-64722
  • METIS-250952
  • EWI-12277

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    Li, R., Kaptein, R., Hiemstra, D., & Kamps, J. (2008). Exploring Topic-based Language Models for Effective Web Information Retrieval. In E. Hoenkamp, M. De Cock, & V. Hoste (Eds.), Proceedings of the Dutch-Belgian Information Retrieval Workshop (DIR 2008) (pp. 65-71). Enschede: Neslia Paniculata.