UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection

TL;DR

This paper presents an unsupervised, language-independent method for detecting lexical semantic change over time, achieving top rankings in SemEval-2020 tasks across multiple languages.

Contribution

The authors introduce a novel unsupervised approach using vector space transformations to detect semantic change, outperforming other methods in SemEval-2020.

Findings

Ranked 1st in binary change detection

Ranked 4th in ranked change detection

Method is fully unsupervised and language independent

Abstract

In this paper, we describe our method for the detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.} We ranked $1^{st}$ in Sub-task 1: binary change detection, and $4^{th}$ in Sub-task 2: ranked change detection. Our method is fully unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and Orthogonal Transformation; and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.