Computational Linguistics and Intelligent Text Processing

Abstract

In this paper we propose a new model of word semantics and similarity that is based on the structural alignment of 〈Subject Verb Object〉 triples extracted from a corpus. The model gives transparent and meaningful representations of word semantics in terms of the predicates asserted of those words in a corpus. The model goes beyond current corpus-based approaches to word similarity in that it reflects the current psychological understanding of similarity as based on structural comparison and alignment. In an assessment comparing the model's similarity scores with those provided by people for 350 word pairs, the model closely matches people's similarity judgments and gives a significantly better fit to people's judgments than that provided by a standard measure of semantic similarity. © 2013 Springer-Verlag.