Modeling metaphor perception with distributional semantics vector space models
|Title||Modeling metaphor perception with distributional semantics vector space models|
|Publication Type||Conference Proceedings|
|Year of Conference||2016|
|Authors||Agres KR, McGregor S, Rataj K, Purver M, Wiggins GA|
|Conference Name||Workshop on Computational Creativity, Concept Invention, and General Intelligence|
|Keywords||computational creativity, distributional semantics, metaphor, vector space models|
In this paper, we present a novel application of a computational model of word meaning to capture human judgments of the linguistic properties of metaphoricity, familiarity, and meaningfulness. We present data gathered from human subjects regarding their ratings of these properties over a set of word pairs specifically designed to exhibit varying degrees of metaphoricity. We then investigate whether these properties can be measured in terms of geometric features of a model of distributional lexical semantics. We compare the performance of two models, our own Concept Discovery Model which dynamically constructs context-sensitive subspaces, and a state-of-the-art static distributional semantic model, and find that our dynamic model performs significantly better in its measurement of metaphoricity.