摘要
【目的】解决专利文本分析中主题模型向高频词倾斜、区分度低的问题。【方法】提出基于词权重方法,形成加权专利文本主题模型,给不同的词分配不同的权重,改变生成模型生成词的概率。【结果】相较于传统的专利文本主题模型,本文提出的加权专利主题模型能够增加主题间的区分度。【局限】加权算法需要更多数据集验证,并不断优化。【结论】通过专利文本数据验证了该方法的可行性与有效性。
[Objective] This study aims to address the issues facing the topic model of patent text analysis such as the inclining to high frequency words and low discrimination rates. [Methods] First, we proposed a word weighting method for the traditional topic model. Then, the modified model assigned different weights to the words, and changed the probability of generating new words. [Results] Compared with traditional methods, the weighted patent topic model could identify the subjects more effectively. [Limitations] The weighting algorithm needs to be validated and optimized with more datasets. [Conclusions] The proposed model could effectively analyze the patent texts.
作者
俞琰
赵乃瑄
Yu Yan;Zhao Naixuan(Information Service Department, Nanjing Tech University, Nanjing 210009, China;Computer Science Department, Southeast University Chengxian College, Nanjing 211816, China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2018年第4期81-89,共9页
Data Analysis and Knowledge Discovery
基金
国家社会科学基金项目"大数据时代支持创新设计的多维度多层次专利文本挖掘研究"(项目编号:17BTQ059)
教育部人文社会科学基金项目"大数据时代技能知识图谱构建研究"(项目编号:16YJAZH073)的研究成果之一
关键词
文本分析
专利
加权主题模型
Text Analysis
Patent
Weighted Topic Model