Unilateral Weighted Jaccard Coefficient for NLP

Abstract

Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various similarity measures are categorized in both syntactic and semantic relationships. In this paper we present a novel similarity, Unilateral Weighted Jaccard Coefficient (uwJaccard), which takes into consideration not only the space among two points but also the semantics among them in a distributional semantic model, the Unilateral Weighted Jaccard Coefficient provides a measure of uncertainty which will be able to measure the uncertainty among sentences such as "man bites dog" and "dog bites man".