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Semantic Change Detection (SCD) of words is an important task for various NLPapplications that must make time-sensitive predictions. Some words are usedover time in novel ways to express new meanings, and these new meaningsestablish themselves as novel senses of existing words. On the other hand, WordSense Disambiguation (WSD) methods associate ambiguous words with sense ids,depending on the context in which they occur. Given this relationship betweenWSD and SCD, we explore the possibility of predicting whether a target word hasits meaning changed between two corpora collected at different time steps, bycomparing the distributions of senses of that word in each corpora. For thispurpose, we use pretrained static sense embeddings to automatically annotateeach occurrence of the target word in a corpus with a sense id. Next, wecompute the distribution of sense ids of a target word in a given corpus.Finally, we use different divergence or distance measures to quantify thesemantic change of the target word across the two given corpora. Ourexperimental results on SemEval 2020 Task 1 dataset show that word sensedistributions can be accurately used to predict semantic changes of words inEnglish, German, Swedish and Latin.

Can Word Sense Distribution Detect Semantic Changes of Words?