The Application of Semantic Similarity in Text Classification

Abstract:

Text classification is a challenging problem which aims to automatically assign unlabeled documents to predefined one or more classes according to its contents. The major problem of text classification is the high dimensionality of the feature space. This paper proposes an approach based on the semantic similarity between the title vectors and the category vectors using the tf*rf weighting method. Experiments show that text classifier based on semantic similarity helps dimension sensitive learning algorithms such as KNN to eliminate the “curse of dimensionality” and as a result makes an important improvement in all categories.

• Periodical: Applied Mechanics and Materials (Volume 346)
• Pages: 141-144
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.346.141
• Online since: August 2013
• Authors: Pei Ying Zhang
• Keywords: K-Nearest Neighbor (KNN) Algorithm, Semantic Similarity, Semantic-Based, Text Classification

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