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Parameters Driving Effectiveness of LSA on Topic Segmentation

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9623))

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Abstract

Latent Semantic Analysis (LSA) is an efficient statistical technique for extracting semantic knowledge from large corpora. One of the major problems of this technique is the identification of the most efficient parameters of LSA and the best combination between them. Therefore, in this paper, we propose a new topic segmenter to study in depth the different parameters of LSA for the topic segmentation. Thus, the aim of this study is to analyze the effect of these different parameters on the quality of topic segmentation and to identify the most efficient parameters. Based on extensive experiments, we showed that the choice of LSA parameters is very sensitive and it has an impact on the quality of topic segmentation. More important, according to this study, we are able to propose appropriate recommendation for the selection of parameters in the field of topic segmentation.

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Acknowledgments

We would like to show our special gratitude to professor emeritus Mouhamed Ben Ahmed, whose has invested his effort in guiding this research and contributed with his precious suggestions, support and encouragement.

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Authors and Affiliations

  1. RIADI laboratory, National School of computer Science (ENSI), University of Manouba, 2010, Manouba, Tunisia

    Marwa Naili, Anja Chaibi Habacha & Henda Hajjami Ben Ghezala

Authors
  1. Marwa Naili
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  2. Anja Chaibi Habacha
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  3. Henda Hajjami Ben Ghezala
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Corresponding author

Correspondence to Anja Chaibi Habacha .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Naili, M., Habacha, A.C., Ben Ghezala, H.H. (2018). Parameters Driving Effectiveness of LSA on Topic Segmentation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_40

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  • DOI: https://doi.org/10.1007/978-3-319-75477-2_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75476-5

  • Online ISBN: 978-3-319-75477-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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