# Measuring Thematic Fit with Distributional Feature Overlap

arXiv: https://arxiv.org/abs/1707.05967 · HTML: https://mlsanity.com/paper/1707.05967/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [Event knowledge in large language models: the gap between the impossible and the unlikely](https://mlsanity.com/paper/2212.01488/) (2022)
- [Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge](https://mlsanity.com/paper/2107.10922/) (2021)
- [Thematic Fit Bits: Annotation Quality and Quantity Interplay for Event Participant Representation](https://mlsanity.com/paper/2105.06097/) (2021)
- [Uncovering Autoregressive LLM Knowledge of Thematic Fit in Event Representation](https://mlsanity.com/paper/2410.15173/) (2024)

## Abstract (arXiv)

In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensions of typical role fillers), and then we compute thematic fit as a weighted overlap between the top features of candidate fillers and role prototypes. Our experiments show that our method consistently outperforms a baseline re-implementing a state-of-the-art system, and achieves better or comparable results to those reported in the literature for the other unsupervised systems. Moreover, it provides an explicit representation of the features characterizing verb-specific semantic roles.