# A Primer on Computational Semantics for Artificial Intelligence Systems

arXiv: https://arxiv.org/abs/2608.25022 · HTML: https://mlsanity.com/paper/2608.25022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 92 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [Do Neural Language Representations Learn Physical Commonsense?](https://mlsanity.com/paper/1908.02899/) (2019)
- [MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks](https://mlsanity.com/paper/2109.06275/) (2021)
- [Few-shot Language Coordination by Modeling Theory of Mind](https://mlsanity.com/paper/2107.05697/) (2021)
- [Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training](https://mlsanity.com/paper/1809.04505/) (2018)
- [Emergence of Theory of Mind Collaboration in Multiagent Systems](https://mlsanity.com/paper/2110.00121/) (2021)
- [Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction](https://mlsanity.com/paper/1706.02757/) (2017)
- [Unsupervised, Bottom-up Category Discovery for Symbol Grounding with a Curious Robot](https://mlsanity.com/paper/2404.03092/) (2024)
- [Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models](https://mlsanity.com/paper/2501.00953/) (2025)
- [On the Computational Modeling of Meaning: Embodied Cognition Intertwined with Emotion](https://mlsanity.com/paper/2307.04518/) (2023)
- [Could the Road to Grounded, Neuro-symbolic AI be Paved with Words-as-Classifiers?](https://mlsanity.com/paper/2507.06335/) (2025)

## Abstract (arXiv)

As people adopt transformer-based language models (e.g., ChatGPT and Gemini) for an increasing number of use-cases, it is important to know how such models learn and represent the meaning of the language, and to be more informed about what language is. This document is an attempt to help the reader understand how linguistic meaning (i.e., semantics) is approached from different fields of scientific and philosophical examination. I also explain three primary semantic theories: formal semantics, grounded semantics, and distributional semantics then compare how transformer-based language models differ from how humans learn language.