TemporalTeller at SemEval‐2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing

Jinan Zhou, Jiaxin LI

Abstract:

This paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical
Semantic Change Detection (Schlechtweg et al., 2020). We develop a unified framework for the
common semantic change detection pipelines including preprocessing, learning word embeddings,
calculating vector distances and determining threshold. We also propose Gamma Quantile
Threshold to distinguish between changed and stable words. Based on our system, we conduct a
comprehensive comparison among BERT, Skip-gram, Temporal Referencing and alignment-based
methods. Evaluation results show that Skip-gram with Temporal Referencing achieves the best
performance of 66.5% classification accuracy and 51.8% Spearman’s Ranking Correlation.