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Article type: Research Article
Authors: Nguyen, Dat Quoca; *; ** | Nguyen, Dai Quocb; ** | Pham, Dang Ducc | Pham, Son Baod
Affiliations: [a] Department of Computing, Macquarie University, Sydney, Australia. E-mail: [email protected] | [b] Department of Computational Linguistics, Saarland University, Saarbrücken, Germany. E-mail: [email protected] | [c] L3S Research Center, University of Hanover, Hanover, Germany. E-mail: [email protected] | [d] VNU University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam. E-mail: [email protected]
Correspondence: [*] Corresponding author. E-mail: [email protected].
Note: [**] The first two authors contributed equally to this work.
Abstract: In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.
Keywords: Natural language processing, part-of-speech tagging, morphological tagging, single classification ripple down rules, rule-based POS tagger, RDRPOSTagger, Bulgarian, Czech, Dutch, English, French, German, Hindi, Italian, Portuguese, Spanish, Swedish, Thai, Vietnamese
DOI: 10.3233/AIC-150698
Journal: AI Communications, vol. 29, no. 3, pp. 409-422, 2016
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