Abstract
Conditional random fields (CRFs) is a framework for building probabilistic models to segment and label sequence data. CRFs offer several advantages over hidden Markov models (HMMs) and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. CRFs also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. In this paper we propose the Language Models developed for Part Of Speech (POS) tagging and chunking using CRFs for Tamil. The Language models are designed based on morphological information. The CRF based POS tagger has an accuracy of about 89.18%, for Tamil and the chunking process performs at an accuracy of 84.25% for the same language.
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Pandian, S.L., Geetha, T.V. (2009). CRF Models for Tamil Part of Speech Tagging and Chunking. In: Li, W., Mollá-Aliod, D. (eds) Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy. ICCPOL 2009. Lecture Notes in Computer Science(), vol 5459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00831-3_2
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DOI: https://doi.org/10.1007/978-3-642-00831-3_2
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