Combining Lexical and Semantic Features for Short Text Classification

In this paper, we propose a novel approach to classify short texts by combining both their lexical and semantic features. We present an improved measurement method for lexical feature selection and furthermore obtain the semantic features with the background knowledge repository which covers target category domains. The combination of lexical and semantic features is achieved by mapping words to topics with different weights. In this way, the dimensionality of feature space is reduced to the number of topics. We here use Wikipedia as background knowledge and employ Support Vector Machine (SVM) as classifier. The experiment results show that our approach has better effectiveness compared with existing methods for classifying short texts.