专利文本主题建模中领域停用词自动选取研究

Automatic Selection of Domain-Specific Stopwords in Topic Model of Patent Text

[目的／意义]针对专利文本主题建模中领域停用词自动选取尚未有充分研究的问题，提出一种新的领域停用词自动选取方法，用于专利文本主题模型分析，以提高专利主题模型的区分度与建模质量。[方法／过程]领域停用词本质上是信息比较少，在不同类别专利文本中区分度低的词。因此，引入辅助专利文本集，使用类别熵衡量词的分布情况，然后依据词的类别熵进行排序，选取类别熵最大的若干词作为领域停用词。[结果／结论]实验通过专利文本数据，验证了该方法的可行性与有效性，能够有效地提高专利主题模型的区分度。

[ Purpose/significance ] Because the research that automatic selection of domain-specific stopwords in topic model of patent text is insufficient, this paper proposes a new method of automatic selection of domain-specific stopwords, for patent text topic model analysis, in order to improve the differentiation and modeling quality of the patent topic model. [ Method/process] In essence, domain-specific stopwords are less important words which contain relatively less information, such words are poorly differentiated in different kinds of patent. Therefore, this paper introduced the auxiliary multi-category patent text dataset and measured the distributions of words through the category entropy. Then, according to the category entropy of words. It chose some words that have the maximum category entropy as the domain-specific stopwords. [ Result/conclusion ] Experimental results show the feasibility and validity of the method proposed in this paper, which can improve the differentiation and quality of topic model for patent text analysis.