Could the Road to Grounded, Neuro-symbolic AI be Paved with Words-as-Classifiers?

2025/07/08 by Casey Kennington, David Schlangen, Kennington, Casey +1
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language and cultural evolution #Multimodal Machine Learning Applications #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2507.06335

openalex publication_date 2025/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Formal, Distributional, and Grounded theories of computational semantics each have their uses and their drawbacks. There has been a shift to ground models of language by adding visual knowledge, and there has been a call to enrich models of language with symbolic methods to gain the benefits from formal, distributional, and grounded theories. In this paper, we attempt to make the case that one potential path forward in unifying all three semantic fields is paved with the words-as-classifier model, a model of word-level grounded semantics that has been incorporated into formalisms and distributional language models in the literature, and it has been well-tested within interactive dialogue settings. We review that literature, motivate the words-as-classifiers model with an appeal to recent work in cognitive science, and describe a small experiment. Finally, we sketch a model of semantics unified through words-as-classifiers.

Citations

• BioXP-0.5B: Explainable Medical-AI via RL-GRPO
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• A Primer in BERTology: What we know about how BERT works
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• Visual Dialog
• Resolving References to Objects in Photographs using the Words-As-Classifiers Model
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• Efficient Estimation of Word Representations in Vector Space
• Minds, brains, and programs

Related

• Grammar-Based Grounded Lexicon Learning
• PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D\n World
• Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models
• Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks
• Composing and Embedding the Words-as-Classifiers Model of Grounded Semantics