Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance

Janosch Haber, Massimo Poesio

Abstract
Co-predication is one of the most frequently used linguistic tests to tell apart shifts in polysemic sense from changes in homonymic meaning. It is increasingly coming under criticism as evidence is accumulating that it tends to mis-classify specific cases of polysemic sense alteration as homonymy. In this paper, we collect empirical data to investigate these accusations. We asses how co-predication acceptability relates to explicit ratings of polyseme word sense similarity, and how well either measure can be predicted through the distance between target words’ contextualised word embeddings. We find that sense similarity appears to be a major contributor in determining co-predication acceptability, but that co-predication judgements tend to rate especially less similar sense interpretations equally as unacceptable as homonym pairs, effectively mis-classifying these instances. The tested contextualised word embeddings fail to predict word sense similarity consistently, but the similarities between BERT embeddings show a significant correlation with co-predication ratings. We take this finding as evidence that BERT embeddings might be better representations of context than encodings of word meaning.
Anthology ID:
2020.starsem-1.12
Volume:
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
*SEM
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
114–124
Language:
URL:
https://aclanthology.org/2020.starsem-1.12
DOI:
Bibkey:
Cite (ACL):
Janosch Haber and Massimo Poesio. 2020. Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, pages 114–124, Barcelona, Spain (Online). Association for Computational Linguistics.
Cite (Informal):
Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance (Haber & Poesio, *SEM 2020)
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