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Semantic indexing of Arabic texts for information retrieval system

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Abstract

As part of information retrieval systems (IRS) and in the context of the use of ontologies for documents and queries indexing, we propose and evaluate in this paper the contribution of this approach applied to Arabic texts. To do this we indexed a corpus of Arabic text using Arabic WordNet. The disambiguation of words was performed by applying the Lesk algorithm. The results obtained by our experiment allowed us to deduct the contribution of this approach in IRS for Arabic texts.

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Notes

  1. CF concept frequency, IDF inverse of document frequency.

  2. Graph where the nodes are the indexing concepts and arcs represent latent contextual relationships between concepts.

  3. http://muchmore.dfki.de/.

  4. Medical subject heading.

  5. http://www.nlm.nih.gov/research/umls/.

  6. http://www.clefcampaign.org/.

  7. http://www.nlm.nih.gov/mesh/.

  8. http://trec.nist.gov/data/t9_filtering.html.

  9. Graphical word is any sequence of characters separated by two spaces.

  10. This time is obtained by Dell Core i5 machine, 4 GB of RAM, 500 GB hard drive.

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Authors and Affiliations

  1. Department of Computer Sciences, Abu Bekr Belkaid University, Tlemcen, Algeria

    Mohammed Alaeddine Abderrahim

  2. Department of Letters and English Language, Mustapha Stambouli University, Mascara, Algeria

    Mohammed Dib

  3. Department of Electrical and Electronic Engineering, Abu Bekr Belkaid University, Tlemcen, Algeria

    Mohammed El-Amine Abderrahim & Mohammed Amine Chikh

Authors
  1. Mohammed Alaeddine Abderrahim
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  2. Mohammed Dib
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  3. Mohammed El-Amine Abderrahim
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  4. Mohammed Amine Chikh
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Correspondence to Mohammed Alaeddine Abderrahim.

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Abderrahim, M.A., Dib, M., Abderrahim, M.EA. et al. Semantic indexing of Arabic texts for information retrieval system. Int J Speech Technol 19, 229–236 (2016). https://doi.org/10.1007/s10772-015-9307-3

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