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A Comparative Study of Standard Part-of-Speech Taggers

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Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) (AI2SD 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 915))

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Abstract

The Part of Speech (PoS) tagging is resolving ambiguity during text processing to assign morphosyntactic tags to each word according to the context. It is an essential task in several fields, particularly corpus linguistics and Natural Language Processing (NLP). Several PoS taggers and tools are already in service as open source or as commercialized solutions. Therefore, deeper investigation regarding their performance is required especially for under-resourced languages like Arabic. Some well-known probabilistic methods were adapted for PoS tagging such as Hidden Markov Models (HMMs), Support Vector Machines (SVM), and Decision Tree (DT). Based on these methods, language-independent PoS taggers have been developed namely TnT, SVMTool, and Treetagger. In fact, this article presents very important topic which concerns, on the one hand, an adaptation of Standard PoS taggers for the Arabic language, and in the other hand conducting very rich and comparative studies and evaluation. Basically, Arabic PoS taggers are very sensitive to the number of the tagsets used and the text form processed, therefore, four different tagsets and two text forms (i.e., Classical and Modern Standard Arabic) have been used.

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Notes

  1. 1.

    https://qutrub.arabeyes.org/.

  2. 2.

    http://oujda-nlp-team.net/en/programms/standard-pos-tagset-arabic-language/.

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Authors and Affiliations

  1. Computer Sciences Laboratory, Faculty of Sciences, Mohammed First University, Oujda, Morocco

    Imad Zeroual & Abdelhak Lakhouaja

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  1. Imad Zeroual
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  2. Abdelhak Lakhouaja
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Correspondence to Imad Zeroual .

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  1. Faculty of Sciences and Techniques, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

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Zeroual, I., Lakhouaja, A. (2019). A Comparative Study of Standard Part-of-Speech Taggers. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_75

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