Combining Syntactic Co-occurrences and Nearest Neighbours in Distributional Methods to Remedy Data Sparseness.

Abstract

The task of automatically acquiring semantically related words have led people to study distributional similarity. The distributional hypothesis states that words that are similar share similar contexts. In this paper we present a technique that aims at improving the performance of a syntax-based distributional method by augmenting the original input of the system (syntactic co-occurrences) with the output of the system (nearest neighbours). This technique is based on the idea of the transitivity of similarity.