Computing semantic similarity between skill statements for approximate matching

Abstract

This paper explores the problem of computing text similarity between verb phrases describing skilled human behavior for the purpose of finding approximate matches. Four parsers are evaluated on a large corpus of skill statements extracted from an enterprise-wide expertise taxonomy. A similarity measure utilizing common semantic role features extracted from parse trees was found superior to an information-theoretic measure of similarity and comparable to the level of human agreement. © 2007 Association for Computational Linguistics.