A probabilistic approach to semantic representation
Published 24 April 2019Open access
Thomas L. Griffiths, Mark Steyvers
Citations212
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TL;DR
This paper illustrates that the large-scale structure of this representation has statistical properties that corre- spond well with those of semantic networks produced by humans, and trace this to the fidelity with which it reproduces the natural statistics of language.
Abstract
Semantic networks produced from human data have statistical properties that cannot be easily captured by spatial representations. We explore a probabilistic approach to semantic representation that explicitly models the probability with which words occur in different contexts, and hence captures the probabilistic relationships between words. We show that this representation has statistical properties consistent with the large-scale structure of semantic networks constructed by humans, and trace the origins of these properties.
Keywords
Computer Science
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