Abstract: Based on a method improved by Latent Semantic Indexing, a topic mining for scientific papers is proposed. This paper describes a process which is used to mine the topics of the scientific papers. It performs conversion, removes non-alphabetic and stop word before further processing. It constructs the term-document matrix based on all words’ weight. It uses modified LSI algorithm to cut the dimension of the matrix and gets a new topic-document matrix. It takes the highest weight of the top five themes as the papers’ topic. This method utilizes the Frobenius norm to regulate matrix, reducing the dimension of the matrix. So the theme of the scientific papers can be mined quickly and accurately.

Key words: latent semantic indexing, topic modeling, scientific documents

摘要： 提出了一种基于潜在语义的科技文献主题挖掘方法，描述了科技文献的主题挖掘模型。对科技文献集进行预处理，计算特征词权重，构造出词汇-文献矩阵。用改进的LSI算法对稀疏矩阵进行降维得到固定的主题-文献矩阵。取权重最高的主题作为该文献的主题。该方法利用Frobenius范数来规范矩阵，对稀疏矩阵进行降维，可以快速精确地挖掘出科技文献的主题。

关键词: 潜在语义索引, 主题挖掘, 科技文献