Apply a rough set-based classifier to dependency parsing

Authors:
Yangsheng Ji;Lin Shang;Xinyu Dai;Ruoce Ma
Affiliations:
State Key Laboratory for Novel Software Technology, Nanjing University, China;State Key Laboratory for Novel Software Technology, Nanjing University, China;State Key Laboratory for Novel Software Technology, Nanjing University, China;State Key Laboratory for Novel Software Technology, Nanjing University, China
Venue:
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Year:
2008

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Abstract

A rough set-based semi-naive Bayesian classification method is applied to dependency parsing, which is an important task in syntactic structure analysis of natural language processing. Many parsing algorithms have emerged combined with statistical machine learning techniques. The rough set-based classifier is embedded with Nivre's deterministic parsing algorithm to conduct dependency parsing task on a Chinese corpus. Experimental results show that the method has a good performance on dependency parsing task. Moreover, the experiments have justified the effectiveness of the classification influence.