A Primer on Computational Semantics for Artificial Intelligence Systems
Not on the map. Why?
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.
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
14 of 92 references.
References · 14
- 2017Attention Is All You Needcited by 60,361
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2018Machine Theory of Mindcited by 226
- 2020The Next Decade in AI: Four Steps Towards Robust Artificial Intelligencecited by 195
- 2019Do Neural Language Representations Learn Physical Commonsense?cited by 62
- 2021MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Taskscited by 62
- 2021Few-shot Language Coordination by Modeling Theory of Mindcited by 32
- 2018Emo2Vec: Learning Generalized Emotion Representation by Multi-task Trainingcited by 11
- 2021Emergence of Theory of Mind Collaboration in Multiagent Systemscited by 8
- 2017Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interactioncited by 3
- 2024Unsupervised, Bottom-up Category Discovery for Symbol Grounding with a Curious Robotcited by 2
- 2025Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Modelscited by 2
Built on by
Not cited yet.