中文词语语义相似度计算——基于《知网》2000

An New Approach Measuring Semantic Similarity in Hownet 2000

词语语义相似度的计算,一种比较常用的方法是使用分类体系的语义词典(如Wordnet)。本文首先利用Hownet中“义原”的树状层次结构,得到“义原”的相似度,再通过“义原”的相似度得到词语(“概念”)的相似度。本文通过引入事物信息量的思想,提出了自己的观点:认为知网中的“义原”对“概念”描述的作用大小取决于其本身所含的语义信息量;“义原”对“概念”的描述划分为直接描述和间接描述两类,并据此计算中文词语语义相似度,在一定程度上得到了和人的直观更加符合的结果。

A basic approach for measuring semantic similarity/distance between words and concepts is to use lexical taxonomy, such as Wordnet. Hownet is a Chinese semantic dictionary, containing abundant semantic information and ontology knowledge, but has quite different construction and architecture. In this paper, we present a new approach using Hownet by drawing in the idea of information theory. We propose that the more semantic information a ＂sememe＂ take, the more powerful it in describing concepts. Then we divide ＂sememe＂ which describes a concept into two set： directly describing part and indirectly describing part. In the experiments, we demonstrate our method have improved performance in measuring semantic similarity between Chinese words.