基于k-means聚类的无导词义消歧  

An Unsupervised Approach to Word Sense Disambiguation Based on HowNet

作　　者：陈浩[1] 何婷婷[1] 姬东鸿[2] 

机构地区：[1]华中师范大学计算机科学系,湖北武汉430079 [2]新加坡信息通讯研究所,新加坡119613

出　　处：《中文信息学报》2005年第4期10-16,共7页Journal of Chinese Information Processing

基　　金：国家语言文字应用委员会"十五"应用项目资助(ZDI105-43B);湖北省自然科学基金资助项目(2001ABB012)

摘　　要：无导词义消歧避免了人工词义标注的巨大工作量,可以适应大规模的多义词消歧工作,具有广阔的应用前景。这篇文章提出了一种无导词义消歧的方法,该方法采用二阶context构造上下文向量,使用k-means算法进行聚类,最后通过计算相似度来进行词义的排歧.实验是在抽取术语的基础上进行的,在多个汉语高频多义词的两组测试中取得了平均准确率82·67%和80·87%的较好的效果。An unsupervised WSD(word sense disambiguation) can avoid big labor cost and it is possible to adjust to deal with large-scale ,so WSD has extensive applications in many fields. This paper presents an unsupervised approach which constructs context vector by means of second-order context, clustering by k-means and disambiguates by calculating the similarity. Our experiments are based on the extraction of term and average accuracy is 82.62% and 80.87% for 8 ambiguous words in open test by this method.

关 键 词：计算机应用 中文信息处理 词义消歧 HOWNET 二阶context K-MEANS聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]