Dimensionality Reduction by Semantic Mapping in Text Categorization

Abstract

In text categorization tasks, the dimensionality reduction become necessary to computation and interpretability of the results generated by machine learning algorithms due to the high-dimensional vector representation of the documents. This paper describes a new feature extraction method called semantic mapping and its application in categorization of web documents. The semantic mapping uses SOM maps to construct variables in reduced space, where each variable describes the behavior of a group of features semantically related. The performance of the semantic mapping is measured and compared empirically with the performance of sparse random mapping and PCA methods and shows to be better than random mapping and a good alternative to PCA.