Ripple Down Rules for Part-of-Speech Tagging
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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