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Empirical evaluation and study of text stemming algorithms

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Abstract

Text stemming is one of the basic preprocessing step for Natural Language Processing applications which is used to transform different word forms into a standard root form. For Arabic script based languages, adequate analysis of text by stemmers is a challenging task due to large number of ambigious structures of the language. In literature, multiple performance evaluation metrics exist for stemmers, each describing the performance from particular aspect. In this work, we review and analyze the text stemming evaluation methods in order to devise criteria for better measurement of stemmer performance. Role of different aspects of stemmer performance measurement like main features, merits and shortcomings are discussed using a resource scarce language i.e. Urdu. Through our experiments we conclude that the current evaluation metrics can only measure an average conflation of words regardless of the correctness of the stem. Moreover, some evaluation metrics favor some type of languages only. None of the existing evaluation metrics can perfectly measure the stemmer performance for all kind of languages. This study will help researchers to evaluate their stemmer using right methods.

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Notes

  1. Built in SMART system.

  2. Extensively modified version Lovens (1968) included in SMART system.

  3. https://antiflux.org/dictionary?dict=moby-thesaurus

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Funding

Funding was provided by Bahauddin Zakariya University (PK) (Grant No: 2019-05).

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad (CUI), Main campus, Park Road, Tarlai Kalan, Islamabad, 45550, Pakistan

    Abdul Jabbar, Manzoor Ilahi Tamimy & Adnan Akhunzada

  2. Department of Computer Science, Bahauddin Zakariya University Multan, Multan, Punjab, Pakistan

    Sajid Iqbal

  3. Bahauddin Zakariya University Multan (Sahiwal Sub-Campus), Multan, Punjab, Pakistan

    Shafiq Hussain

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  1. Abdul Jabbar
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Jabbar, A., Iqbal, S., Tamimy, M.I. et al. Empirical evaluation and study of text stemming algorithms. Artif Intell Rev 53, 5559–5588 (2020). https://doi.org/10.1007/s10462-020-09828-3

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