NDLI: Japanese dependency analysis using fuzzy support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Huiwei Zhou Degen Huang Tong Yu Dalian
Copyright Year	2009
Description	Author affiliation: Dalian University of Technology Dalian, Liaoning, China (Huiwei Zhou; Degen Huang; Tong Yu Dalian)
Abstract	This paper introduces Fuzzy Support Vector Machines (FSVMs) for Japanese dependency analysis. Japanese dependency analysis based on Support Vector Machines (SVMs) has been proposed and has achieved high accuracy. While regular SVMs try to find a decision hyperplane from two distinct classes of the input examples, FSVMs apply a fuzzy membership to each input example such that different examples can make different contributions to the decision hyperplane. For nonlinear classification problem, FSVMs can achieve good performance by reducing the effect of outliers. In this paper, a new fuzzy membership function is proposed to Japanese dependency analysis. We train an initial classifier with a small training set. The fuzzy membership is calculated by the distance from each input example to the initial hyperplane. In addition, we employ Nivre's algorithm for Japanese dependency analysis since it parses a sentence in linear-time. Experiments using the Kyoto University Corpus show that the parser using Nivre's algorithm outperforms the previous systems, and the proposed FSVMs improve the already excellent performance of SVMs for Japanese dependency analysis.
Starting Page	1
Ending Page	7
File Size	283915
Page Count	7
File Format	PDF
ISBN	9781424445387
DOI	10.1109/NLPKE.2009.5313776
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Nivre's Algorithm Fuzzy Support Vector Machines (FSVMs) Natural languages Support Vector Machines (SVMs) History Support vector machines Tree graphs Training data Inference algorithms Robustness Performance analysis Large-scale systems Japanese dependency analysis
Content Type	Text
Resource Type	Article

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