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An effective short text conceptualization based on new short text similarity

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Abstract

Recently short text messages, tweets, comments and so on, have become a large portion of the online text data. They are limited in length and different from traditional documents in their shortness and sparseness. As a result, short text tends to be ambiguous and its degree is not the same for all languages; and as Arabic is a very high flexional language, where a single word can have multiple meanings, the short text representation plays a vital role in any Text Mining task. To address these issues, we propose an efficient representation for short text based on concepts instead of terms using BabelNet as an external knowledge. However, in the conceptualization process, while searching polysemic term-corresponding concepts, multiple matches are detected. Therefore, assigning a term to a concept is a crucial step and we believe that short text similarity can be useful to overcome the problem of mapping term to the corresponding concept. In this paper, we reintroduce Web-based Kernel function for measuring the semantic relatedness between concepts to disambiguate an expression versus multiple concepts. The proposed method has been evaluated using an Arabic short text categorization system and the obtained results illustrate the interest of our contribution.

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Notes

  1. https://developers.google.com/custom-search/docs/start.

  2. https://datamarket.azure.com/dataset/5BA839F1-12CE-4CCE-BF57-A49D98D29A44.

  3. http://www.dmoz.org.

  4. http://social-dynamics.org/twitter-network-data/.

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Authors and Affiliations

  1. LISA Laboratory, ENSA, USMBA, Fez, Morocco

    Mohammed Bekkali

  2. ENSA, AEU, Tangier, Morocco

    Abdelmonaime Lachkar

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  1. Mohammed Bekkali
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  2. Abdelmonaime Lachkar
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Correspondence to Mohammed Bekkali.

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Bekkali, M., Lachkar, A. An effective short text conceptualization based on new short text similarity. Soc. Netw. Anal. Min. 9, 1 (2019). https://doi.org/10.1007/s13278-018-0544-8

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  • DOI: https://doi.org/10.1007/s13278-018-0544-8

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