Towards pertinent evaluation methodologies for word-space models

Abstract

This paper discusses evaluation methodologies for a particular kind of meaning models known as word-space models, which use distributional information to assemble geometric representations of meaning similarities. Word-space models have received considerable attention in recent years, and have begun to see employment outside the walls of computational linguistics laboratories. However, the evaluation methodologies of such models remain infantile, and lack efforts at standardization. Very few studies have critically assessed the methodologies used to evaluate word spaces. This paper attempts to fill some of this void. It is the central goal of this paper to answer the question “how can we determine whether a given word space is a good word space?”

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