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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