关键词: 潜在语义索引, Sigmiod函数, 位置因子, 权重算法

Abstract: Latent Semantic Indexing (LSI) is a new document retrieval model that has been developed during the last ten years. It is easy to compute and requires less human intervention. Term weighting, which is a difficult problem and of great importance in LSI, was studied in detail. In view of the most popular term weighting algorithms, TF-IDF, which is unreasonable to make use of linear and unable to emphasize the significance of key terms which contribute mainly to the content of a text, a new weighting design based on Sigmiod function and location factor was proposed. The new method highlights the importance of the different terms in documents and is in more favor of constructing the latent semantic space. It was tested in the experimental platform named "Chinese LSI Retrieval Analysis System", and the results show that the new method enhances the performance of LSI information retrieve.

Key words: Latent Semantic Indexing, Sigmiod function, location factor, weighting algorithms