Linguistic Symbolism in ML: Language, Meaning, and Representation

The relationship between symbolic and connectionist approaches to artificial intelligence has been a foundational tension in the field for decades. This paper proposes a hybrid account where symbols can emerge from structured transformations over distributed representations in contemporary AI systems. I develop operational tests for symbolic competence and evaluate evidence from large language models, retrieval-augmented generation, and neuro-symbolic architectures. The framework distinguishes between functional symbolic behavior and genuine symbolic representation while providing criteria for assessing when AI systems exhibit symbolic competence. Case studies in compositional generalization, systematic reasoning, and grounding problems demonstrate how hybrid approaches can advance our understanding of language, meaning, and representation in machines.