# A Structured Distributional Model of Sentence Meaning and Processing

arXiv: https://arxiv.org/abs/1906.07280 · HTML: https://mlsanity.com/paper/1906.07280/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Event knowledge in large language models: the gap between the impossible and the unlikely](https://mlsanity.com/paper/2212.01488/) (2022)
- [CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Models](https://mlsanity.com/paper/2112.11941/) (2021)
- [Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models](https://mlsanity.com/paper/2403.14859/) (2024)
- [Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge](https://mlsanity.com/paper/2107.10922/) (2021)
- [Uncovering Autoregressive LLM Knowledge of Thematic Fit in Event Representation](https://mlsanity.com/paper/2410.15173/) (2024)
- [Modeling the Developmental Shift in Telicity Acquisition](https://mlsanity.com/paper/2609.17996/) (2026)

## Abstract (arXiv)

Most compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a Structured Distributional Model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations. The semantic representation of a sentence is a formal structure derived from Discourse Representation Theory and containing distributional vectors. This structure is dynamically and incrementally built by integrating knowledge about events and their typical participants, as they are activated by lexical items. Event knowledge is modeled as a graph extracted from parsed corpora and encoding roles and relationships between participants that are represented as distributional vectors. SDM is grounded on extensive psycholinguistic research showing that generalized knowledge about events stored in semantic memory plays a key role in sentence comprehension. We evaluate SDM on two recently introduced compositionality datasets, and our results show that combining a simple compositional model with event knowledge constantly improves performances, even with different types of word embeddings.