Performance and accuracy analysis of semantic kernel functions. Issue 1 (1st February 2016)

• Record Type: Journal Article
• Authors: Manuja, Manoj; Garg, Deepak
• Is Part Of: Program. Volume 50:Issue 1(2016)
• Page Start: 83
• Page End: 102
• Publication Date: 2016-02-01
• Subjects: Support vector machines -- SVM -- Semantic kernel function -- Text classification; Libraries, University and college -- Great Britain -- Automation -- Periodicals; 025.30285
• DOI: 10.1108/PROG-04-2014-0028
• Languages: English
• ISSNs: 0033-0337

Abstract

Abstract : Purpose: – Syntax-based text classification (TC) mechanisms have been overtly replaced by semantic-based systems in recent years. Semantic-based TC systems are particularly useful in those scenarios where similarity among documents is computed considering semantic relationships among their terms. Kernel functions have received major attention because of the unprecedented popularity of SVMs in the field of TC. Most of the kernel functions exploit syntactic structures of the text, but quite a few also use a priori semantic information for knowledge extraction. The purpose of this paper is to investigate semantic kernel functions in the context of TC. Design/methodology/approach: – This work presents performance and accuracy analysis of seven semantic kernel functions (Semantic Smoothing Kernel, Latent Semantic Kernel, Semantic WordNet-based Kernel, Semantic Smoothing Kernel having Implicit Superconcept Expansions, Compactness-based Disambiguation Kernel Function, Omiotis-based S-VSM semantic kernel function and Top-k S-VSM semantic kernel) being implemented with SVM as kernel method. All seven semantic kernels are implemented in SVM-Light tool. Findings: – Performance and accuracy parameters of seven semantic kernel functions have been evaluated and compared. The experimental results show that Top-k S-VSM semantic kernel has the highest performance and accuracy among all the evaluated kernel functions which make it a preferred building block for kernel methods for TC and retrieval. Research limitations/implications: – A combination of semantic kernel function with syntactic kernel function needs to be investigated as there is a scope of further improvement in terms of accuracy and performance in all the seven semantic kernel functions. Practical implications: – This research provides an insight into TC using a priori semantic knowledge. Three commonly used data sets are being exploited. It will be quite interesting to explore these kernel functions on live web data which may test their actual utility in real business scenarios. Originality/value: – Comparison of performance and accuracy parameters is the novel point of this research paper. To the best of the authors' knowledge, this type of comparison has not been done previously.