Discrete and Probabilistic Classifier-based Semantics
We present a formal semantics (a version of Type Theory with Records) which places classifiers of perceptual information at the core of semantics. Using this framework, we present an account of the interpretation and classification of utterances referring to perceptually available situations (such as visual scenes). The account improves on previous work by clarifying the role of classifiers in a hybrid semantics combining statistical/neural classifiers with logical/inferential aspects of meaning. The account covers both discrete and probabilistic classification, thereby enabling learning, vagueness and other non-discrete linguistic phenomena.
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