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S-LDA: Documents Classification Enrichment for Information Retrieval

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Advances in Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1653))

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Abstract

In recent years, the research on topic modeling techniques has become a hot topic among researchers thanks to their ability to classify and understand a large text corpora which has a beneficial effect on information retrieval performance, but recently user queries are more complicated because they need to know not only which documents are most helpful to them, but also which parts of documents are more or less related to their request. Also, they need to search by topic or document, not merely by keywords.

In this context, we propose a new approach of automated text classification based on LDA topic modeling algorithm and the rich semantic document structure which helps to semantically enrich the generated classes by indexing them in the documents sections according to their probabilities distribution and visualize them through a hyper-graph.

Experiments have been conducted to measure the effectiveness of our solution compared to topic modeling classification approaches based on text content only. The results show the superiority of our approach.

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Notes

  1. 1.

    https://www.nltk.org/.

  2. 2.

    https://pypi.org/project/gensim/.

  3. 3.

    https://dev.springernature.com/.

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Author information

Authors and Affiliations

  1. FST, University of Manar, SERCOM, Tunis, Tunisia

    Amani Drissi

  2. VPNC Lab., FSJEGJ, University of Jendouba, 8189, Jendouba, Tunisia

    Anis Tissaoui & Salma Sassi

  3. E2S UPPA, LIUPPA, University Pau & Pays Adour, EA3000, Anglet, France

    Richard Chbeir

  4. INSAT, University of Carthage, SERCOM, Tunis, Tunisia

    Abderrazak Jemai

Authors
  1. Amani Drissi
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  2. Anis Tissaoui
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  3. Salma Sassi
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  4. Richard Chbeir
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  5. Abderrazak Jemai
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Corresponding author

Correspondence to Amani Drissi .

Editor information

Editors and Affiliations

  1. University of Craiova, Craiova, Romania

    Costin Bădică

  2. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. Claude Bernard University Lyon 1, Villeurbanne Cedex, France

    Djamal Benslimane

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Marek Krótkiewicz

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Drissi, A., Tissaoui, A., Sassi, S., Chbeir, R., Jemai, A. (2022). S-LDA: Documents Classification Enrichment for Information Retrieval. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_56

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  • DOI: https://doi.org/10.1007/978-3-031-16210-7_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16209-1

  • Online ISBN: 978-3-031-16210-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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