How to marry a star: Probabilistic constraints for meaning in context
By K. Erk, Aurélie Herbelot. 2020
8 citations recorded by the source corpus.
In this paper, we derive a notion of word meaning in context that characterizes meaning as both intensional and conceptual. We introduce a framework for specifying local as well as global constraints on word meaning in context, together with their interactions, thus modelling a wide range of lexical shifts and ambiguities observed in utterance interpretation. We represent sentence meaning as a situation description system, a probabilistic model which takes utterance understanding to be the mental process of describing to oneself one or more situations that would account for an observed utterance. We show how the system can be implemented in practice, and apply it to examples containing various contextualisation phenomena.
Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods, Computational Linguistics & Semantics
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Citations in the corpus
- Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (2022, background): “It is even possible to explore the tensions between competing word senses in examples such as the astronomer married a star, where the presence of astronomer invokes the heavenly body sense of star but the act of marrying invokes (more plausibly) the sense of a movie star [65].”