How to marry a star: probabilistic constraints for meaning in context
This work addresses the challenge of utterance interpretation in natural language processing, but it appears incremental as it builds on existing probabilistic and constraint-based approaches without claiming broad SOTA improvements.
The paper tackles the problem of defining word meaning in context by characterizing it as both intensional and conceptual, and introduces a framework with probabilistic constraints to model lexical shifts and ambiguities, resulting in a practical implementation applied to examples of contextualization phenomena.
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 the 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.