Modeling Metaphor Perception Using Dynamically Contextual Distributional Semantics.

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ID: 38033
2019
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Ranked #279 of 510 articles by views in Frontiers in psychology

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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 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.
Reference Key
mcgregor2019frontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors McGregor, Stephen;Agres, Kat;Rataj, Karolina;Purver, Matthew;Wiggins, Geraint;
Journal Frontiers in psychology
Year 2019
DOI
10.3389/fpsyg.2019.00765
URL
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