Accurately Extracting Coherent Relevant Passages Using Hidden Markov Models

Author

Jing JIANG, University of Illinois at Urbana-ChampaignFollow
ChengXiang ZHAI, University of Illinois at Urbana-Champaign

Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2005

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.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management, October 31 - November 5, 2005, Bremen, Germany

First Page

289

Last Page

290

ISBN

9781595931405

Identifier

10.1145/1099554.1099631

Publisher

ACM

City or Country

Bremen, Germany

Citation

JIANG, Jing and ZHAI, ChengXiang. Accurately Extracting Coherent Relevant Passages Using Hidden Markov Models. (2005). CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management, October 31 - November 5, 2005, Bremen, Germany. 289-290.
Available at: https://ink.library.smu.edu.sg/sis_research/1257

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

http://dx.doi.org/10.1145/1099554.1099631