基于偏旁部首知识表示学习的汉字字形相似度计算方法

A Method of Chinese Character Glyph Similarity Calculation Based on Radical Knowledge Representation Learning

该文提出了一种字形相似度计算方法,旨在解决汉字中相似字形(称作形似字)的识别和查找问题。首先,提出了汉字拆分方法,并构建了偏旁部首知识图谱;然后,基于图谱和汉字的结构特点,提出2CTransE模型,学习汉字实体语义信息的表示;最后,将输出的实体向量用于汉字字形的相似度计算,得到目标汉字的形似字候选集。实验结果表明,该文所提出的方法对于不同结构汉字的字形相似度计算有一定效果,所形成的汉字部件组成库,为之后字形计算的相关研究提供了行之有效的数据集。同时,也拓宽了日语等类汉语语言文字字体相似度计算的研究思路。

The paper proposes a method for calculating the similarity of character glyphs,which aims to solve the problem of identifying similar Chinese characters.First,we construct a radical knowledge graph according to the character’s composition.Then,based on the knowledge graph and structure features,the paper proposes 2 CTransE to learn the semantic representation of entities.Finally,we calculate the character similarity by the entity vector.Results show that the method are effective in similar characters identification.And the component library can be used in the subsequent related researches.We also propose a novel method for Japanese and other similar languages in character similarity calcalation.