@article {2019,
	title = {Re-Representing Metaphor: Modeling Metaphor Perception Using Dynamically Contextual Distributional Semantics},
	journal = {Frontiers in Psychology: Cognitive Science},
	volume = {10},
	year = {2019},
	month = {04/2019},
	pages = {1-18},
	type = {Original Research},
	abstract = {In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic
approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process.},
	keywords = {computational creativity, computational linguistics, conceptual models, distributional semantics, metaphor, vector space models},
	doi = {doi: 10.3389/fpsyg.2019.00765},
	author = {Stephen McGregor and Kat Agres and Karolina Rataj and Matthew Purver and Geraint Wiggins}
}
