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Learning Semantic Representation from Experience and Language

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If you have a question about this talk, please contact Dr. Luna Filipovic.

For queries about CILR and this talk please contact Dr. Luna Filipovic

We present an account of semantic representation that focuses on distinct types of information from which meaning can be learned. In particular, we argue that there are at least two major types of information from which we can learn meaning. The first is what we call experiential information. This is data derived both from our sensory- motor interactions with the outside world, as well as from our experience of own inner states, particularly our emotions. The second type of information is language-based. In particular, it is derived from the general linguistic context in which words appear. Semantic representations are given, then, by combining these two types of information. In the talk we will first provide an overview of this proposal, and then present a Bayesian model detailing how these two types of information can be integrated with one another in a statistically optimal manner.

This talk is part of the Cambridge Institute for Language Research events series.

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