# How to marry a star: probabilistic constraints for meaning in context

arXiv: https://arxiv.org/abs/2009.07936 · HTML: https://mlsanity.com/paper/2009.07936/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [Schr"odinger's Bat: Diffusion Models Sometimes Generate Polysemous Words in Superposition](https://mlsanity.com/paper/2211.13095/) (2022)
- [We went to look for meaning and all we got were these lousy representations: aspects of meaning representation for computational semantics](https://mlsanity.com/paper/2109.04949/) (2021)
- [Contextualized word senses: from attention to compositionality](https://mlsanity.com/paper/2312.00680/) (2023)
- [Conversational Implicatures: Modelling Relevance Theory Probabilistically](https://mlsanity.com/paper/2509.22354/) (2025)

## Abstract (arXiv)

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.