Abstract
Part Of Speech (POS) tagging is the ability to computationally determine which POS of a word is activated by its use in a particular context. It is a useful preprocessing tool in many natural languages processing (NLP) applications. In this paper, we expose a new Arabic POS Tagger based on the combination of two main modules: the 1st order Markov and a decision tree models. These two modules allow improving existing POS Taggers with the possibility of tagging unknown words. The tag set used for this POS is an elementary tag set composed of 4 tags {noun, verb, particle, punctuation} that are sufficient for some NLP applications but greatly help increasing the accuracy. The POS tagger has been trained with the NEMLAR corpus. The experiment results demonstrate its efficiency with an overall accuracy of 98% for the full system.
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Notes
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Transliterated using Buckwalter [6].
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Affixes represent the prefixes, infixes and suffixes indicate substrings that come respectively at the beginning middle and at the end of a word.
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Corpus available at https://github.com/kdarwish/Farasa.
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Tnaji, K., Bouzoubaa, K., Aouragh, S.L. (2021). A Light Arabic POS Tagger Using a Hybrid Approach. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_19
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DOI: https://doi.org/10.1007/978-3-030-73882-2_19
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