Understanding context-dependent variation in word meanings is a key aspect of
human language comprehension supported by the lexicon. Lexicographic resources
(e.g., WordNet) capture only some of this context-dependent variation; for
example, they often do not encode how closely senses, or discretized word
meanings, are related to one another. Our work investigates whether recent
advances in NLP, specifically contextualized word embeddings, capture
human-like distinctions between English word senses, such as polysemy and
homonymy. We collect data from a behavioral, web-based experiment, in which
participants provide judgments of the relatedness of multiple WordNet senses of
a word in a two-dimensional spatial arrangement task. We find that
participants' judgments of the relatedness between senses are correlated with
distances between senses in the BERT embedding space. Homonymous senses (e.g.,
bat as mammal vs. bat as sports equipment) are reliably more distant from one
another in the embedding space than polysemous ones (e.g., chicken as animal
vs. chicken as meat). Our findings point towards the potential utility of
continuous-space representations of sense meanings.

本文探讨了自然语言处理中的一项工作，基于 BERT 嵌入空间来评估英语单词的多义和同音词意义之间的相关性。发现使用 BERT 嵌入模型能够在意义表示上更加清晰地捕捉多义性和同音词意的潜在结构，具有潜在的应用价值。