skip to main content
10.1145/860435.860505acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
Article

Investigating the relationship between language model perplexity and IR precision-recall measures

Published:28 July 2003Publication History

ABSTRACT

An empirical study has been conducted investigating the relationship between the performance of an aspect based language model in terms of perplexity and the corresponding information retrieval performance obtained. It is observed, on the corpora considered, that the perplexity of the language model has a systematic relationship with the achievable precision recall performance though it is not statistically significant.

References

  1. A. Berger and J. D. Lafferty. Information retrieval as statistical translation. In Research and Development in Information Retrieval pages 222--229, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research 3(5):993--1022, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Girolami and A. Kaban. On an equivalence between PLSI and LDA. In Proceedings of SIGIR 2003 SIGIR, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. T. Hofmann. Probabilistic Latent Semantic Indexing. In Proceedings of the 22nd Annual ACM Conference on Research and Development in Information Retrieval pages 50--57, Berkeley, California, August 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. V. Lavrenko and W. B. Croft. Relevance-based language models. In Research and Development in Information Retrieval pages 120--127, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. E. L. Margulis. Modeling documents with multiple poisson distributions. Information Processing and Management 29(2):215--227, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Ponte and W. Croft. A language modeling approach to information retrieval. In Proceedings of SIGIR 98 pages 275--281. SIGIR, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. F. Song and W. B. Croft. A general language model for information retrieval (poster abstract). In Research and Development in Information Retrieval pages 279--280, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Investigating the relationship between language model perplexity and IR precision-recall measures

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
        July 2003
        490 pages
        ISBN:1581136463
        DOI:10.1145/860435

        Copyright © 2003 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 28 July 2003

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Author Tags

        Qualifiers

        • Article

        Acceptance Rates

        SIGIR '03 Paper Acceptance Rate46of266submissions,17%Overall Acceptance Rate792of3,983submissions,20%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader