A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

Autor:	Nguyen, Dat Quoc, Nguyen, Dai Quoc, Pham, Dang Duc, Pham, Son Bao
Rok vydání:	2014
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
DOI:	10.3233/AIC-150698
Popis:	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. Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1412.4021 Zobrazit plný text záznamu View this record from Arxiv