Japanese Dependency Analysis using Fuzzy Support Vector Machines

摘要：

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.

关键词： Japanese dependency analysis Support Vector Machines (SVMs) Fuzzy Support Vector Machines (FSVMs) Nivres Algorithm

作者: Huiwei ZHOU Degen HUANG Tong YU

作者单位: Dalian University of Technology Dalian, Liaoning, China

会议类型: 国际会议

会议名称: International Conference on Natural Language Processing and Knowledge Engineering(IEEE自然语言处理与知识工程国际会议 IEEE NLP-KE 2009)

会议地点: 大连

会议语种:英文

页码: 1-7

在线出版日期: 2009-09-24（万方平台首次上网日期，不代表论文的发表时间）