Categorization of Malay documents using latent semantic indexing - Ab Samat, Nordianah; Azmi Murad, Masrah Azrifah; Atan, Rodziah; Abdullah, Muhamad Taufik (Universiti Utara Malaysia, 2008)
Document categorization is a widely researched area of information retrieval. A popular approach to categorize documents is the Vector Space Model (VSM), which represents texts with feature vectors. The categorizing based on the VSM suffers from noise caused by synonymy and polysemy. Thus, an approach for the clustering of Malay documents based on semantic relations between words is proposed in this paper. The method is based on the model first formulated in the context of information retrieval, called Latent Semantic Indexing (LSI). This model leads to a vector representation of each document using Singular Value Decomposition (SVD), where familiar clustering techniques can be applied in this space. LSI produced good document clustering by obtaining relevant subjects appearing in a cluster.