Abstract
This paper represents results for a part of speech tagger based on Maximum Entropy model on Urdu corpora. We discussed the specialized features/parameters of the model and their impact on tagger performance. We also discussed the complexity of Urdu language for a tagger to predict correct tag and inconsistencies in corpora found during experiments. For the purpose of detailed experiments, two different corpora are used in this paper. The maximum accuracy recorded for tagger is 93.24% overall and 69.89% on previously unseen data.
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Mohy Ud Din, U., Anwar, M.W., Mallah, G.A. (2020). Maximum Entropy Based Urdu Part of Speech Tagging. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_41
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DOI: https://doi.org/10.1007/978-981-15-5232-8_41
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