Accurately extracting coherent relevant passages using hidden Markov models

Jing Jiang, Cheng Xiang Zhai

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

Abstract

In this paper, we present a principled method for accurately extracting coherent relevant passages of variable lengths using HMMs. We show that with appropriate parameter estimation, the HMM method outperforms a number of strong baseline methods on two data sets.

Original languageEnglish (US)
Title of host publicationCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages289-290
Number of pages2
ISBN (Print)1595931406, 9781595931405
DOIs
StatePublished - 2005
EventCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management - Bremen, Germany
Duration: Oct 31 2005Nov 5 2005

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

OtherCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
CountryGermany
CityBremen
Period10/31/0511/5/05

Keywords

  • Hidden Markov Models
  • Passage Retrieval

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Fingerprint Dive into the research topics of 'Accurately extracting coherent relevant passages using hidden Markov models'. Together they form a unique fingerprint.

  • Cite this

    Jiang, J., & Zhai, C. X. (2005). Accurately extracting coherent relevant passages using hidden Markov models. In CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management (pp. 289-290). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/1099554.1099631