Evaluation of Background Knowledge for Latent Semantic Indexing Classification

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This paper presents work that evaluates background knowledge for use in improving accuracy for text classification using Latent Semantic Indexing (LSI). LSI’s singular value decomposition process can be performed on a combination of training data and background knowledge. Intuitively, the closer the background knowledge is to the classification task, the more helpful it will be in terms of creating a reduced space that will be effective in performing classification. Using a variety of data sets, we evaluate sets of background knowledge in terms of how close they are to training data, and in terms of how much they improve classification.