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

Kwok Chung Au; Kwok Wai Cheung

doi:10.1007/978-3-540-24775-3_70

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

Kwok Chung Au, Kwok Wai Cheung

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-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 language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
Editors	Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang
Publisher	Springer Verlag
Pages	590-594
Number of pages	5
ISBN (Print)	354022064X, 9783540220640
DOIs	https://doi.org/10.1007/978-3-540-24775-3_70
Publication status	Published - 2004
Event	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia Duration: 26 May 2004 → 28 May 2004 https://link.springer.com/book/10.1007/b97861 (Conference proceedings)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3056
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
Abbreviated title	PAKDD 2004
Country/Territory	Australia
City	Sydney
Period	26/05/04 → 28/05/04
Internet address	https://link.springer.com/book/10.1007/b97861 (Conference proceedings)

Scopus Subject Areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-24775-3_70

Cite this

Au, K. C., & Cheung, K. W. (2004). Learning hidden markov model topology based on KL divergence for information extraction. In H. Dai, R. Srikant, & C. Zhang (Eds.), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings (pp. 590-594). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056). Springer Verlag. https://doi.org/10.1007/978-3-540-24775-3_70

Au, Kwok Chung ; Cheung, Kwok Wai. / Learning hidden markov model topology based on KL divergence for information extraction. Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. editor / Honghua Dai ; Ramakrishnan Srikant ; Chengqi Zhang. Springer Verlag, 2004. pp. 590-594 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Learning hidden markov model topology based on KL divergence for information extraction",

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.",

author = "Au, {Kwok Chung} and Cheung, {Kwok Wai}",

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Au, KC & Cheung, KW 2004, Learning hidden markov model topology based on KL divergence for information extraction. in H Dai, R Srikant & C Zhang (eds), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3056, Springer Verlag, pp. 590-594, 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004, Sydney, Australia, 26/05/04. https://doi.org/10.1007/978-3-540-24775-3_70

Learning hidden markov model topology based on KL divergence for information extraction. / Au, Kwok Chung; Cheung, Kwok Wai.
Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. ed. / Honghua Dai; Ramakrishnan Srikant; Chengqi Zhang. Springer Verlag, 2004. p. 590-594 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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N2 - 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.

AB - 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.

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SN - 9783540220640

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Au KC, Cheung KW. Learning hidden markov model topology based on KL divergence for information extraction. In Dai H, Srikant R, Zhang C, editors, Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Springer Verlag. 2004. p. 590-594. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24775-3_70

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

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