Li, J., & Armstrong, B. C. (2023). Probing the Representational
Structure of Regular Polysemy in a Contextual Word Embedding Model via
Sense Analogy Questions. Proceedings of the Annual Meeting of the
Cognitive Science Society.
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Abstract
Regular polysemes are sets of ambiguous words that all share the same relationship between their meanings, such as CHICKEN and LOBSTER both referring to an animal or its meat. To probe how a context embedding model, here exemplified by BERT, represents regular polysemy, we analyzed whether its embeddings support answering sense analogy questions similar to “is the mapping between CHICKEN (as an animal) and CHICKEN (as a meat) the same as that which maps between LOBSTER (as an animal) to LOBSTER (as a meat)?” We found that (1) the model was sensitive to the shared structure within a regularity type; (2) the shared structure varies across regularity types, potentially reflective of a “regularity continuum;” (3) some high-order latent structure may be shared across regularity types, suggestive of a similar latent structure across types; and (4) there is equivocal evidence that the aforementioned effects are explained by meaning overlap.
Keywords: regular polysemy; semantic ambiguity; word analogy; contextual word embeddings; lexical semantics; BERT model
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