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Text segmentation via topic modeling: an analytical study

Authors:

François Yvon,

Joemon M. Jose,

Olivier CappeAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1553 - 1556

https://doi.org/10.1145/1645953.1646170

Published: 02 November 2009 Publication History

Abstract

In this paper, the task of text segmentation is approached from a topic modeling perspective. We investigate the use of latent Dirichlet allocation (LDA) topic model to segment a text into semantically coherent segments. A major benefit of the proposed approach is that along with the segment boundaries, it outputs the topic distribution associated with each segment. This information is of potential use in applications like segment retrieval and discourse analysis. The new approach outperforms a standard baseline method and yields significantly better performance than most of the available unsupervised methods on a benchmark dataset.

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Cited By

Avdeeva ZGavrilov MLemtyuzhnikova DSharafiev A(2024)Methods for Solving the Problem of Topic Segmentation of Texts Based on Knowledge GraphsJournal of Computer and Systems Sciences International10.1134/S106423072470047363:4(642-662)Online publication date: 24-Nov-2024
https://doi.org/10.1134/S1064230724700473
Ma JHan SYe SWang G(2024)Enhanced Document Segmentation at Paragraph Level2024 6th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT)10.1109/ECNCT63103.2024.10704470(528-535)Online publication date: 19-Jul-2024
https://doi.org/10.1109/ECNCT63103.2024.10704470
Maraj AVargas Martin MMakrehchi M(2024)Coherence Graphs: Bridging the Gap in Text Segmentation with Unsupervised LearningNatural Language Processing and Information Systems10.1007/978-3-031-70242-6_14(139-149)Online publication date: 20-Sep-2024
https://doi.org/10.1007/978-3-031-70242-6_14
Show More Cited By

Index Terms

Text segmentation via topic modeling: an analytical study
1. Applied computing
  1. Document management and text processing

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Published In

cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 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: 02 November 2009

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November 2 - 6, 2009

Hong Kong, China

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Avdeeva ZGavrilov MLemtyuzhnikova DSharafiev A(2024)Methods for Solving the Problem of Topic Segmentation of Texts Based on Knowledge GraphsJournal of Computer and Systems Sciences International10.1134/S106423072470047363:4(642-662)Online publication date: 24-Nov-2024
https://doi.org/10.1134/S1064230724700473
Ma JHan SYe SWang G(2024)Enhanced Document Segmentation at Paragraph Level2024 6th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT)10.1109/ECNCT63103.2024.10704470(528-535)Online publication date: 19-Jul-2024
https://doi.org/10.1109/ECNCT63103.2024.10704470
Maraj AVargas Martin MMakrehchi M(2024)Coherence Graphs: Bridging the Gap in Text Segmentation with Unsupervised LearningNatural Language Processing and Information Systems10.1007/978-3-031-70242-6_14(139-149)Online publication date: 20-Sep-2024
https://doi.org/10.1007/978-3-031-70242-6_14
Murauer BTschuggnall MSpecht GBrandl J(2023)Algorithmic Segmentation of Job Ads Using Textual AnalysisComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-23804-8_23(287-300)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-23804-8_23
Zhao ZZhang LLian XLv H(2023)DRIP: Segmenting individual requirements from software requirement documentsSoftware: Practice and Experience10.1002/spe.330354:5(842-874)Online publication date: 19-Dec-2023
https://doi.org/10.1002/spe.3303
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
Bakhri IUtami EHartanto A(2022)The Influnce Stemmer Truncating and Statistical in Mapping Students Research Trends with Latent Dirichlet Allocation (LDA)2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)10.1109/ICITISEE57756.2022.10057809(57-62)Online publication date: 13-Dec-2022
https://doi.org/10.1109/ICITISEE57756.2022.10057809
Rahimi MZahedi MMashayekhi H(2022)A Probabilistic Topic Model based on Short Distance Co-occurrencesExpert Systems with Applications10.1016/j.eswa.2022.116518(116518)Online publication date: Jan-2022
https://doi.org/10.1016/j.eswa.2022.116518
Lin TSun AWang Y(2022)EDU-Capsule: aspect-based sentiment analysis at clause levelKnowledge and Information Systems10.1007/s10115-022-01797-z65:2(517-541)Online publication date: 6-Dec-2022
https://doi.org/10.1007/s10115-022-01797-z
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
https://doi.org/10.1007/978-981-16-7136-4_22
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