A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection

Detecting temporal semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. Lexical Semantic Change Detection (SCD) task considers the problem of predicting whether a given target word, w, changes its meaning between two different text corpora, C₁ and C₂. For this purpose, we propose a supervised two-staged SCD method that uses existing Word-in-Context (WiC) datasets. In the first stage, for a target word w, we learn two sense-aware encoder that represents the meaning of w in a given sentence selected from a corpus. Next, in the second stage, we learn a sense-aware distance metric that compares the semantic representations of a target word across all of its occurrences in C₁ and C₂. Experimental results on multiple benchmark datasets for SCD show that our proposed method consistently outperforms all previously proposed SCD methods for multiple languages, establishing a novel state-of-the-art for SCD. Interestingly, our findings imply that there are specialised dimensions that carry information related to semantic changes of words in the sense-aware embedding space. Source code is available at https: //github. com/a1da4/svp-sdml.