A Simple Semantic Kernel Approach for SVM using Higher-Order Paths

Abstract

The bag of words (BOW) representation of documents is very common in text classification systems. However, the BOW approach ignores the position of the words in the document and more importantly, the semantic relations between the words. In this study, we present a simple semantic kernel for Support Vector Machines (SVM) algorithm. This kernel uses higher-order relations between terms in order to incorporate semantic information into the SVM. This is an easy to implement algorithm which forms a basis for future improvements. We perform a serious of experiments on different well known textual datasets. Experiment results show that classification performance improves over the traditional kernels used in SVM such as linear kernel which is commonly used in text classification.