Abstract
This paper presents a new approach to learn a rule based system for the task of part of speech tagging. Our approach is based on an incremental knowledge acquisition methodology where rules are stored in an exception-structure and new rules are only added to correct errors of existing rules; thus allowing systematic control of interaction between rules. Experimental results of our approach on English show that we achieve in the best accuracy published to date: 97.095% on the Penn Treebank corpus. We also obtain the best performance for Vietnamese VietTreeBank corpus.
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Nguyen, D.Q., Nguyen, D.Q., Pham, S.B., Pham, D.D. (2011). Ripple Down Rules for Part-of-Speech Tagging. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_15
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DOI: https://doi.org/10.1007/978-3-642-19400-9_15
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