Ripple down rules for part-of-speech tagging

Authors:
Dat Quoc Nguyen;Dai Quoc Nguyen;Son Bao Pham;Dang Duc Pham
Affiliations:
Human Machine Interaction Laboratory, Faculty of Information Technology, University of Engineering and Technology, Vietnam National University, Hanoi;Human Machine Interaction Laboratory, Faculty of Information Technology, University of Engineering and Technology, Vietnam National University, Hanoi;Human Machine Interaction Laboratory, Faculty of Information Technology, University of Engineering and Technology and Information Technology Institute, Vietnam National University, Hanoi;Human Machine Interaction Laboratory, Faculty of Information Technology, University of Engineering and Technology, Vietnam National University, Hanoi
Venue:
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Year:
2011

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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.