# Usage-based learning of grammatical categories

arXiv: https://arxiv.org/abs/2204.10201 · HTML: https://mlsanity.com/paper/2204.10201/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Built on by (most cited first, 3 of 3)

- [Construction Grammar and Artificial Intelligence](https://mlsanity.com/paper/2309.00135/) (2023)
- [Unsupervised Acquisition of Discrete Grammatical Categories](https://mlsanity.com/paper/2503.18702/) (2025)
- [A Knowledge-Based Language Model: Deducing Grammatical Knowledge in a Multi-Agent Language Acquisition Simulation](https://mlsanity.com/paper/2512.02195/) (2025)

## Abstract (arXiv)

Human languages use a wide range of grammatical categories to constrain which words or phrases can fill certain slots in grammatical patterns and to express additional meanings, such as tense or aspect, through morpho-syntactic means. These grammatical categories, which are most often language-specific and changing over time, are difficult to define and learn. This paper raises the question how these categories can be acquired and where they have come from. We explore a usage-based approach. This means that categories and grammatical constructions are selected and aligned by their success in language interactions. We report on a multi-agent experiment in which agents are endowed with mechanisms for understanding and producing utterances as well as mechanisms for expanding their inventories using a meta-level learning process based on pro- and anti-unification. We show that a categorial type network which has scores based on the success in a language interaction leads to the spontaneous formation of grammatical categories in tandem with the formation of grammatical patterns.