A HowNet-based Semantic Relatedness Kernel for Text Classification

The exploitation of the semantic relatedness kernel has always been an appealing subject in the context of text retrieval and information management. Typically, in text classification the documents are represented in the vector space using the bag-of-words (BOW) approach. The BOW approach does not take into account the semantic relatedness information. To further improve the text classification performance, this paper presents a new semantic-based kernel of support vector machine algorithm for text classification. This method firstly using CHI method to select document feature vectors, secondly calculates the feature vector weights using TF-IDF method, and utilizes the semantic relatedness kernel which involves the semantic similarity computation and semantic relevance computation to classify the document using support vector machines. Experimental results show that compared with the traditional support vector machine algorithm, the algorithm in the text classification achieves improved classification F1-measure.