Enhancing relevance models with adaptive passage retrieval

Xiaoyan Li, Zhigang Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on some heterogeneous collections. Previous research has shown that combining passage-level evidence with pseudo relevance feedback brings added benefits. In this paper, we study passage retrieval with relevance models in the language-modeling framework for document retrieval. An adaptive passage retrieval approach is proposed to document ranking based on the best passage of a document given a query. The proposed passage ranking method is applied to two relevance-based language models: the Lavrenko-Croft relevance model and our robust relevance model. Experiments are carried out with three query sets on three different collections from TREC. Our experimental results show that combining adaptive passage retrieval with relevance models (particularly the robust relevance model) consistently outperforms solely applying relevance models on full-length document retrieval.

Original languageEnglish (US)
Title of host publicationAdvances in Information Retrieval - 30th European Conference on IR Research, ECIR 2008, Proceedings
Pages463-471
Number of pages9
DOIs
StatePublished - Apr 14 2008
Externally publishedYes
Event30th Annual European Conference on Information Retrieval, ECIR 2008 - Glasgow, United Kingdom
Duration: Mar 30 2008Apr 3 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4956 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference30th Annual European Conference on Information Retrieval, ECIR 2008
CountryUnited Kingdom
CityGlasgow
Period3/30/084/3/08

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Language modeling
  • Passage retrieval
  • Relevance models

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  • Cite this

    Li, X., & Zhu, Z. (2008). Enhancing relevance models with adaptive passage retrieval. In Advances in Information Retrieval - 30th European Conference on IR Research, ECIR 2008, Proceedings (pp. 463-471). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4956 LNCS). https://doi.org/10.1007/978-3-540-78646-7_44