Papers › UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
Ondřej Pražák, Pavel Přibáň, Stephen Taylor, Jakub Sido
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 1ˢᵗ in Sub-task 1: binary change detection, and 4ᵗʰ 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.
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