Papers › A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection

A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection

1 Mar 2024arXiv:2403.00226archive 2025-07-28

Taichi Aida, Danushka Bollegala

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 involves 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 encoders that represent 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 achieves strong performance in multiple languages. Additionally, our method achieves significant improvements on WiC benchmarks compared to a sense-aware encoder with conventional distance functions. Source code is available at https://github.com/LivNLP/svp-sdml .

Code

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections