The Unnatural Language ToolKit (ULTK)

Introduction

ULTK is an open-source Python library for computational semantic typology research. ULTK's key features include unifying data structures, algorithms for generating artificial languages, and data analysis tools for related computational experiments.

Read the documentation.

Installing ULTK

First, set up a virtual environment (e.g. via miniconda, conda create -n ultk python=3.11, and conda activate ultk).

1. Download or clone this repository and navigate to the root folder.

2. Install ULTK (We recommend doing this inside a virtual environment)

   pip install -e .

Getting started

• Check out the examples, starting with a simple efficient communication analysis of indefinites and modals.
• For an introduction to efficient communication research, here is a survey paper of the field.
• For an introduction to the RSA framework, see this online textbook.

Modules

There are two modules. The first is ultk.effcomm, which includes methods for measuring informativity of languages and/or communicative success of Rational Speech Act agents, and for language population sampling and optimization w.r.t Pareto fronts.

The second module is ultk.language, which contains primitives for constructing semantic spaces, expressions, and languages. It also has a grammar module which can be used for building expressions in a Language of Thought and measuring complexity in terms of minimum description length, as well as for natural language syntax.

The source code is available on github here.

Testing

Unit tests are written in pytest and executed via running pytest in the src/tests folder.

References

Figures:

Kemp, C. & Regier, T. (2012). Kinship Categories Across Languages Reflect General Communicative Principles. Science. https://www.science.org/doi/10.1126/science.1218811

Zaslavsky, N., Kemp, C., Regier, T., & Tishby, N. (2018). Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences, 115(31), 7937–7942. https://doi.org/10.1073/pnas.1800521115

Denić, M., Steinert-Threlkeld, S., & Szymanik, J. (2022). Indefinite Pronouns Optimize the Simplicity/Informativeness Trade-Off. Cognitive Science, 46(5), e13142. https://doi.org/10.1111/cogs.13142

Steinert-Threlkeld, S. (2021). Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals. Entropy, 23(10), Article 10. https://doi.org/10.3390/e23101335

Links:

Imel, N., & Steinert-Threlkeld, S. (2022). Modal semantic universals optimize the simplicity/informativeness trade-off. Semantics and Linguistic Theory, 1(0), Article 0. https://doi.org/10.3765/salt.v1i0.5346

Kemp, C., Xu, Y., & Regier, T. (2018). Semantic Typology and Efficient Communication. Annual Review of Linguistics, 4(1), 109–128. https://doi.org/10.1146/annurev-linguistics-011817-045406

Citation

@article{imel2025ultk,
  author    = {Imel, Nathaniel and Haberland, Chris and Steinert-Threlkeld, Shane},
  title     = {The Unnatural Language ToolKit (ULTK)},
  journal   = {Proceedings of the Society for Computation in Linguistics},
  volume    = {8},
  number    = {1},
  pages     = {46},
  year      = {2025},
  doi       = {10.7275/scil.3144},
  url       = {https://doi.org/10.7275/scil.3144}
}