| Summary: | The term ‘meaning’, as it is presently employed in Linguistics, is a polysemous concept, covering a broad range of operational definitions. Focussing on two of these definitions, meaning as ‘concept’ and meaning as ‘context’ (also known as ‘distributional semantics’), this paper explores to what extent these operational definitions lead to converging conclusions regarding the number and nature of distinct senses a polysemous form covers. More specifically, it investigates whether the sense network that emerges from the principled polysemy model of over as proposed by Tyler & Evans (2003; 2001) can be reconstructed by the neural language model BERT. The study assesses whether the contextual information encoded in BERT embeddings can be employed to succesfully (i) recognize the abstract sense categories and (ii) replicate the relative distances between the senses of over proposed in the principled polysemy model. The results suggest that, while there is partial convergence, the two models ultimately lead to different global abstractions because the imagistic information that plays a key role in conceptual approaches to prepositional meaning may not be encoded in contextualized word embeddings.
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