Axiomatic analysis of translation language model for information retrieval

Maryam Karimzadehgan, Chengxiang Zhai

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

Abstract

Statistical translation models have been shown to outperform simple document language models which rely on exact matching of words in the query and documents. A main challenge in applying translation models to ad hoc information retrieval is to estimate a translation model without training data. In this paper, we perform axiomatic analysis of translation language model for retrieval in order to gain insights about how to optimize the estimation of translation probabilities. We propose a set of constraints that a reasonable translation language model should satisfy. We check these constraints on the state-of-the-art translation estimation method based on Mutual Information and find that it does not satisfy most of the constraints. We then propose a new estimation method that better satisfies the defined constraints. Experimental results on representative TREC data sets show that the proposed new estimation method outperforms the existing Mutual Information-based estimation, suggesting that the proposed constraints are indeed helpful for designing better estimation methods for translation language model.

Original languageEnglish (US)
Title of host publicationAdvances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings
Pages268-280
Number of pages13
DOIs
StatePublished - 2012
Event34th European Conference on Information Retrieval, ECIR 2012 - Barcelona, Spain
Duration: Apr 1 2012Apr 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7224 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other34th European Conference on Information Retrieval, ECIR 2012
Country/TerritorySpain
CityBarcelona
Period4/1/124/5/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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