Semantic relatedness computation is a well known problem with multidisciplinary applications. Existing approaches to computing semantic relatedness ignore the asymmetric associations of words. In the absence of an explicit topical context, these asymmetric associations can be effectively used to represent the relation of words in directional con- texts. Motivated by the idea of word associations, this paper presents a new approach to computing semantic relatedness using asymmetric association based probabilities of words extracted from the directional contexts of words based on the Wikipedia corpus. The performance eval- uation of the proposed approach on a variety of publicly available bench- mark datasets shows that the asymmetric association based measures outperformed not only the baseline symmetric measures but also most of the state-of-art approaches.
['Shahida Jabeen', 'Xiaoying Gao', 'Peter Andreae']
Probabilistic Associations as a Proxy for Semantic Relatedness