# Unsupervised Discovery of Unaccusative and Unergative Verbs

arXiv: https://arxiv.org/abs/2111.00808 · HTML: https://mlsanity.com/paper/2111.00808/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Verb Argument Structure Alternations in Word and Sentence Embeddings](https://mlsanity.com/paper/1811.10773/) (2018)

## Built on by (most cited first, 1 of 1)

- [How Abstract Is Linguistic Generalization in Large Language Models? Experiments with Argument Structure](https://mlsanity.com/paper/2311.04900/) (2023)

## Abstract (arXiv)

We present an unsupervised method to detect English unergative and unaccusative verbs. These categories allow us to identify verbs participating in the causative-inchoative alternation without knowing the semantic roles of the verb. The method is based on the generation of intransitive sentence variants of candidate verbs and probing a language model. We obtained results on par with similar approaches, with the added benefit of not relying on annotated resources.