Learning hidden markov model topology based on KL divergence for information extraction

Kwok Chung Au, Kwok Wai CHEUNG

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

To locate information embedded in documents, information extraction systems based on rule-based pattern matching have long been used. To further improve the extraction generalization, hidden Markov model (HMM) has recently been adopted for modeling temporal variations of the target patterns with promising results. In this paper, a state-merging method is adopted for learning the topology with the use of a localized Kullback Leibler (KL) divergence. The proposed system has been applied to a set of domain-specific job advertisements and preliminary experiments show promising results.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
EditorsHonghua Dai, Ramakrishnan Srikant, Chengqi Zhang
PublisherSpringer Verlag
Pages590-594
Number of pages5
ISBN (Print)354022064X, 9783540220640
DOIs
Publication statusPublished - 2004
Event8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia
Duration: 26 May 200428 May 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3056
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
Country/TerritoryAustralia
CitySydney
Period26/05/0428/05/04

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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