Sprinkled latent semantic indexing for text classification with background knowledge

Abstract

In text classification, one key problem is its inherent dichotomy of polysemy and synonym; the other problem is the insufficient usage of abundant useful, but unlabeled text documents. Targeting on solving these problems, we incorporate a sprinkling Latent Semantic Indexing (LSI) with background knowledge for text classification. The motivation comes from: 1) LSI is a popular technique for information retrieval and it also succeeds in text classification solving the problem of polysemy and synonym; 2) By fusing the sprinkling terms and unlabeled terms, our method not only considers the class relationship, but also explores the unlabeled information. Finally, experimental results on text documents demonstrate our proposed method benefits for improving the classification performance. © 2009 Springer Berlin Heidelberg.