A simple semantic kernel approach for SVM using higher-order paths

Abstract

Ganiz, Murat Can (Dogus Author) -- Conference full title: 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2014) : Alberobello, Italy, 23-25 June 2014.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

A corpus-based semantic kernel for text classification by using meaning values of terms

“…This is a problem since it is not considering semantic relations between terms. This can be addressed by incorporating semantic information between words using semantic kernels as described in Altınel et al (2013Altınel et al ( , 2014aAltınel et al ( , 2014b, Bloehdorn et al (2006), Kandola et al (2004), Luo et al (2011), Nasir et al (2011, Siolas and d'Alché-Buc (2000), Tsatsaronis et al (2010), Wang and Domeniconi (2008) and Wang et al (2014).…”

“…Wang et al (2014) experimentally show that their diffusion matrix exploits higher-order co-occurrences to capture latent semantic relationships between terms in the WSD tasks from SensEval. In our previous studies (Altınel et al, 2013(Altınel et al, , 2014a(Altınel et al, , 2014b we built semantic kernels for SVM by taking advantages of higher-order paths. There are numerous systems with higher-order co-occurrences in text classification.…”

“…Benefits of using on higher-order paths between documents (Altınel et al, 2014a) and between terms (Altınel et al, 2014b;Ganiz et al, 2009;Poyraz et al, 2014) are demonstrated in Fig. 1.…”

“…dot product), the similarity value between documents d 1 and d 3 will be zero since they do share any terms. But this measure is misleading since these two documents have some connections in the context of the dataset over d 2 (Altınel et al, 2014b) as it can be perceived in Fig. 1.…”

“…In our previous studies, we proposed several corpus-based semantic kernels such as Higher-Order Semantic Kernel (HOSK) (Altınel et al, 2013), Iterative Higher-Order Semantic Kernel (IHOSK) (Altınel et al, 2014a) and Higher-Order Term Kernel (HOTK) (Altınel et al, 2014b) for SVM. In these studies, we showed significant improvements on classification performance over traditional kernels of SVM such as linear kernel, polynomial kernel and RBF kernel by taking advantage of higher-order relations between terms and documents.…”