Abstract
Part of speech (POS) tagging is one of the basic preprocessing techniques for any text processing NLP application. It is a difficult task for morphologically rich and partially free word order languages. This paper describes a Part of Speech (POS) tagger of one such morphologically rich language, Tamil. The main issue of POS tagging is the ambiguity that arises because different POS tags can have the same inflections, and have to be disambiguated using the context. This paper presents a pattern based bootstrapping approach using only a small set of POS labeled suffix context patterns. The pattern consists of a stem and a sequence of suffixes, obtained by segmentation using a suffix list. This bootstrapping technique generates new patterns by iteratively masking suffixes with low probability of occurrences in the suffix context, and replacing them with other co-occurring suffixes. We have tested our system with a corpus containing 20,000 Tamil documents having 2,71,933 unique words. Our system achieves a precision of 87.74%.
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Ganesh, J., Parthasarathi, R., Geetha, T.V., Balaji, J. (2014). Pattern Based Bootstrapping Technique for Tamil POS Tagging. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_25
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DOI: https://doi.org/10.1007/978-3-319-13817-6_25
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