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Modern AI language models rely on mathematical representations of words derived from their context. Firth's mid-20th-century theories provided the core conceptual foundation that enables machine learning models to capture semantic relationships from co-occurrence patterns.
Firth's framework rejected rigid, single-system analyses in favour of polysystematism and situational context. By linking meaning strictly to textual and situational companionship, he anticipated modern distributional semantics. Decades later, this perspective directly informed the creation of dense vector embeddings, proving that foundational linguistic theories from the 1950s remain deeply relevant to current NLP architectures.
Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.
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