基于k-means聚类的无导词义消歧

An Unsupervised Approach to Word Sense Disambiguation Based on HowNet

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.