An Assessment on Character-based Chinese News Filtering Using Latent Semantic Indexing

We assess the Latent Semantic Indexing (LSI) approach to Chinese information filtering. In particular, the approach is for Chinese news filtering agents that use a character-based and hierarchical filtering scheme. The traditional vector space model is employed as an information filtering model, and each document is converted into a vector of weights of terms. Instead of using words as terms in the JR nominating tradition, terms refer to Chinese characters. LSI captures the semantic relationship between documents and Chinese characters. We use the Sin-gular-value Decomposition (SVD) technique to compress the term space into a lower dimension which achieves latent association between documents and terms. The results of experiments show that the recall and precision rates of Chinese news filtering using the character-based ap-proach incorporating the LSI technique are satisfactory.