Article

Topic-based document segmentation with probabilistic latent semantic analysis

Authors:

Thorsten Brants,

Ioannis TsochantaridisAuthors Info & Claims

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

Pages 211 - 218

https://doi.org/10.1145/584792.584829

Published: 04 November 2002 Publication History

Abstract

This paper presents a new method for topic-based document segmentation, i.e., the identification of boundaries between parts of a document that bear on different topics. The method combines the use of the Probabilistic Latent Semantic Analysis (PLSA) model with the method of selecting segmentation points based on the similarity values between pairs of adjacent blocks. The use of PLSA allows for a better representation of sparse information in a text block, such as a sentence or a sequence of sentences. Furthermore, segmentation performance is improved by combining different instantiations of the same model, either using different random initializations or different numbers of latent classes. Results on commonly available data sets are significantly better than those of other state-of-the-art systems.

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Cited By

Figuera PGarcía Bringas P(2024)Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and InsightsTechnologies10.3390/technologies1201000512:1(5)Online publication date: 3-Jan-2024
https://doi.org/10.3390/technologies12010005
Wang GGarg PWu W(2024)Segmented Summarization and Refinement: A Pipeline for Long-Document Analysis on Social MediaJournal of Social Computing10.23919/JSC.2024.00105:2(132-144)Online publication date: Jun-2024
https://doi.org/10.23919/JSC.2024.0010
Repp SHaffner EGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Dynamic Segmentation for Efficient Retrieval of Podcasts: The Repping AlgorithmProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658047(29-36)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658047
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Index Terms

Topic-based document segmentation with probabilistic latent semantic analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

November 2002

704 pages

ISBN:1581134924

DOI:10.1145/584792

General Chair:
Charles Nicholas
University of Maryland Baltimore County
,
Program Chairs:
David Grossman
Illinois Institute of Technology
,
Konstantinos Kalpakis
University of Maryland Baltimore County
,
Sajda Qureshi
Erasmus University, Rotterdam
,
Han van Dissel
Erasmus University, Rotterdam
,
Len Seligman
The MITRE Corporation

Copyright © 2002 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]

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Published: 04 November 2002

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CIKM02: Eleventh ACM International Conference on Information and Knowledge Management

November 4 - 9, 2002

Virginia, McLean, USA

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Cited By

Figuera PGarcía Bringas P(2024)Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and InsightsTechnologies10.3390/technologies1201000512:1(5)Online publication date: 3-Jan-2024
https://doi.org/10.3390/technologies12010005
Wang GGarg PWu W(2024)Segmented Summarization and Refinement: A Pipeline for Long-Document Analysis on Social MediaJournal of Social Computing10.23919/JSC.2024.00105:2(132-144)Online publication date: Jun-2024
https://doi.org/10.23919/JSC.2024.0010
Repp SHaffner EGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Dynamic Segmentation for Efficient Retrieval of Podcasts: The Repping AlgorithmProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658047(29-36)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658047
Wang YLiu LYuan CLi MLiu J(2024)Negative-Sensitive Framework With Semantic Enhancement for Composed Image RetrievalIEEE Transactions on Multimedia10.1109/TMM.2024.336989826(7608-7621)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3369898
Wang ZTang XLiu JYing Z(2022)Subtask analysis of process data through a predictive modelBritish Journal of Mathematical and Statistical Psychology10.1111/bmsp.1229076:1(211-235)Online publication date: Nov-2022
https://doi.org/10.1111/bmsp.12290
Li JChiu BShang SShao L(2022)Neural Text Segmentation and its Application to Sentiment AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.298336034:2(828-842)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TKDE.2020.2983360
Yazdi SNajar FBouguila N(2022)Bayesian Folding-In Using Generalized Dirichlet and Beta-Liouville Kernels for Information Retrieval2022 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI51031.2022.10022072(1430-1435)Online publication date: 4-Dec-2022
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Daroch SSingh P(2022)An Analysis of Various Text Segmentation ApproachesProceedings of International Conference on Intelligent Cyber-Physical Systems10.1007/978-981-16-7136-4_22(285-302)Online publication date: 24-Jan-2022
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Iikura ROkada MMori N(2021)Paragraph Boundary Recognition in Novels for Story UnderstandingApplied Sciences10.3390/app1112563211:12(5632)Online publication date: 18-Jun-2021
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Wu QHare AWang STu YLiu ZBrinton CLi Y(2021)BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and SegmentationACM Transactions on Intelligent Systems and Technology10.1145/346826812:5(1-29)Online publication date: 15-Oct-2021
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