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Part of speech tagging for Arabic


This paper presents an investigation of part of speech (POS) tagging for Arabic as it occurs naturally, i.e. unvocalized text (without diacritics). We also do not assume any prior tokenization, although this was used previously as a basis for POS tagging. Arabic is a morphologically complex language, i.e. there is a high number of inflections per word; and the tagset is larger than the typical tagset for English. Both factors, the second one being partly dependent on the first, increase the number of word/tag combinations, for which the POS tagger needs to find estimates, and thus they contribute to data sparseness. We present a novel approach to Arabic POS tagging that does not require any pre-processing, such as segmentation or tokenization: whole word tagging. In this approach, the complete word is assigned a complex POS tag, which includes morphological information. A competing approach investigates the effect of segmentation and vocalization on POS tagging to alleviate data sparseness and ambiguity. In the segmentation-based approach, we first automatically segment words and then POS tags the segments. The complex tagset encompasses 993 POS tags, whereas the segment-based tagset encompasses only 139 tags. However, segments are also more ambiguous, thus there are more possible combinations of segment tags. In realistic situations, in which we have no information about segmentation or vocalization, whole word tagging reaches the highest accuracy of 94.74%. If gold standard segmentation or vocalization is available, including this information improves POS tagging accuracy. However, while our automatic segmentation and vocalization modules reach state-of-the-art performance, their performance is not reliable enough for POS tagging and actually impairs POS tagging performance. Finally, we investigate whether a reduction of the complex tagset to the Extra-Reduced Tagset as suggested by Habash and Rambow (Habash, N., and Rambow, O. 2005. Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), Ann Arbor, MI, USA, pp. 573–80) will alleviate the data sparseness problem. While the POS tagging accuracy increases due to the smaller tagset, a closer look shows that using a complex tagset for POS tagging and then converting the resulting annotation to the smaller tagset results in a higher accuracy than tagging using the smaller tagset directly.

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D. Aha , D. Kibler , and M. K. Albert 1991. Instance-based learning algorithms. Machine Learning 6: 3766.

K. Beesley 1996. Arabic finite-state morphological analysis and generation. In Proceedings of the 16th International Conference on Computational Linguistics (COLING), Copenhagen, Denmark, pp. 8994.

W. Daelemans , A. van den Bosch , and J. Zavrel 1999. Forgetting exceptions is harmful in language learning. Machine Learning 34 (1–3): 1143 (special issue on Natural Language Learning).

K. Darwish 2002. Building a shallow Arabic morpholgical analyzer in one day. In Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages, Philadelphia, PA.

Y. Gal 2002. An HMM approach to vowel restoration in Arabic and Hebrew. In Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages, Philadelphia, PA.

N. Habash , O. Rambow , and G. Kiraz 2005. Morphological analysis and generation for Arabic dialects. In Proceedings of the ACL Workshop on Semitic Languages, Ann Arbor, MI, pp. 1724.

R. Nelken , and S. Shieber 2005. Arabic diacritization using weighed finite-state transducers. In Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages, Ann Arbor, MI, pp. 7986.

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Natural Language Engineering
  • ISSN: 1351-3249
  • EISSN: 1469-8110
  • URL: /core/journals/natural-language-engineering
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