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Topic segmentation with an aspect hidden Markov model

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SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 343 - 348

https://doi.org/10.1145/383952.384021

Published: 01 September 2001 Publication History

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Abstract

We present a novel probabilistic method for topic segmentation on unstructured text. One previous approach to this problem utilizes the hidden Markov model (HMM) method for probabilistically modeling sequence data [7]. The HMM treats a document as mutually independent sets of words generated by a latent topic variable in a time series. We extend this idea by embedding Hofmann's aspect model for text [5] into the segmenting HMM to form an aspect HMM (AHMM). In doing so, we provide an intuitive topical dependency between words and a cohesive segmentation model. We apply this method to segment unbroken streams of New York Times articles as well as noisy transcripts of radio programs on SpeechBot, an online audio archive indexed by an automatic speech recognition engine. We provide experimental comparisons which show that the AHMM outperforms the HMM for this task.

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Daniel Gildea and Thomas Hofmann.Topic-based language models using em.EuroSpeech-99,pages 2167 -2170,1999.

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Thomas Hofmann.Probabilistic latent semantic indexing.Proceedings of the Twenty-Second Annual International SIGIRConference on Research and Development in Information Retrieval,1999.

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Hosseiny Marani ABaumer E(2023)A Review of Stability in Topic Modeling: Metrics for Assessing and Techniques for Improving StabilityACM Computing Surveys10.1145/362326956:5(1-32)Online publication date: 27-Nov-2023
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Topic segmentation with an aspect hidden Markov model

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Published In

SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

September 2001

454 pages

ISBN:1581133316

DOI:10.1145/383952

Chairmen:
Donald H. Kraft
Louisiana State Univ.
,
W. Bruce Croft
University of Massachusetts, (For the Americas)
,
David J. Harper
The Robert Gordon University, (For Europe and Africa)
,
Justin Zobel
RMIT University, (For Asia and Australasia)

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Published: 01 September 2001

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SIGIR01: 24th ACM/SIGIR International Conference on Research and Development in Information Retrieval

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SIGIR '01 Paper Acceptance Rate 47 of 201 submissions, 23%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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