基于《知网》的词语相似度算法研究

Study on HowNet-Based Word Similarity Algorithm

基于《知网》的词语(句子)相似度计算通常是把义原(词语)之间的最优匹配做为运算的基本单位的,最终的整体相似度数值可由每一部分的相似度值通过适当的加权计算合成而来,这样的做法往往会造成一些匹配对的信息重复和结构不合理。针对这个问题,该文通过统计出两个直接义原集合间的共有信息(共性)和差异信息(个性)来计算集合的相似度,并把此方法引入到词语(句子)的相似度计算中去。最终的实验比对结果表明该文所采用的方法更为稳定和有效。

Word（sentence） similarity computing based on the ＂HowNet＂ usually treats the optimal matches between the primitives or words as the basic unit,and the ultimate outcome can be the sum of weighted counts.However,this approach often results in the information duplication and some irrational constructions.To deal with these issues,this paper propose to calculate the similarity of sets by the statistics on common information（commonality） and the different information（differences） between the two sets of direct primitives.Moreover,the paper introduces this measure into the calculation of sentence similarity.The final experimental analysis shows that the proposed method is more stable and effective.