Can Word Sense Distribution Detect Semantic Changes of Words?
Xiaohang Tang Yi Zhou Taichi Aida Procheta Sen Danushka Bollegala
Semantic Change Detection (SCD) of words is an important task for various NLP
applications that must make time-sensitive predictions. Some words are used
over time in novel ways to express new meanings, and these new meanings
establish themselves as novel senses of existing words. On the other hand, Word
Sense Disambiguation (WSD) methods associate ambiguous words with sense ids,
depending on the context in which they occur. Given this relationship between
WSD and SCD, we explore the possibility of predicting whether a target word has
its meaning changed between two corpora collected at different time steps, by
comparing the distributions of senses of that word in each corpora. For this
purpose, we use pretrained static sense embeddings to automatically annotate
each occurrence of the target word in a corpus with a sense id. Next, we
compute the distribution of sense ids of a target word in a given corpus.
Finally, we use different divergence or distance measures to quantify the
semantic change of the target word across the two given corpora. Our
experimental results on SemEval 2020 Task 1 dataset show that word sense
distributions can be accurately used to predict semantic changes of words in
English, German, Swedish and Latin.