Contextualized Word Embeddings Encode Aspects of Human-Like Word Sense Knowledge
This addresses the problem of modeling context-dependent word meanings for NLP researchers, offering insights into sense representation but is incremental in comparing embeddings to human data.
The study investigated whether contextualized word embeddings capture human-like distinctions between word senses, finding that distances in BERT embeddings correlate with human judgments of sense relatedness, with homonymous senses being more distant than polysemous ones.
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.