UAlberta at LSCDiscovery: Lexical Semantic Change Detection via Word Sense Disambiguation
Daniela Teodorescu, Spencer von der Ohe, Grzegorz Kondrak · 2022
We describe our two systems for the shared task on Lexical Semantic Change Discovery in Spanish.For binary change detection, we frame the task as a word sense disambiguation (WSD) problem.We derive sense frequency distributions for target words in both old and modern corpora.We assume that the word semantics have changed if a sense is observed in only one of the two corpora, or the relative change for any sense exceeds a tuned threshold.For graded change discovery, we follow the design of CIRCE (Pömsl and Lyapin, 2020) by combining both static and contextual embeddings.For contextual embeddings, we use XLM-RoBERTa instead of BERT, and train the model to predict a masked token instead of the time period.Our language-independent methods achieve results that are close to the bestperforming systems in the shared task.