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Most words are ambiguous--i.e., they convey distinct meanings in differentcontexts--and even the meanings of unambiguous words are context-dependent.Both phenomena present a challenge for NLP. Recently, the advent ofcontextualized word embeddings has led to success on tasks involving lexicalambiguity, such as Word Sense Disambiguation. However, there are few tasks thatdirectly evaluate how well these contextualized embeddings accommodate the morecontinuous, dynamic nature of word meaning--particularly in a way that matcheshuman intuitions. We introduce RAW-C, a dataset of graded, human relatednessjudgments for 112 ambiguous words in context (with 672 sentence pairs total),as well as human estimates of sense dominance. The average inter-annotatoragreement (assessed using a leave-one-annotator-out method) was 0.79. We thenshow that a measure of cosine distance, computed using contextualizedembeddings from BERT and ELMo, correlates with human judgments, but that cosinedistance also systematically underestimates how similar humans find uses of thesame sense of a word to be, and systematically overestimates how similar humansfind uses of different-sense homonyms. Finally, we propose a synthesis betweenpsycholinguistic theories of the mental lexicon and computational models oflexical semantics.

RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)