Categorization of Malay documents using latent semantic indexing
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...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2008
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/11286/1/87-91-CR74.pdf http://repo.uum.edu.my/11286/ http://www.kmice.uum.edu.my |
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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | 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 o
f 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. |
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