UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
This work provides an unsupervised and language-independent method for researchers and linguists to detect how word meanings evolve over time, which is an incremental improvement in the field.
This paper describes a method for detecting lexical semantic change by comparing semantic differences of words across two corpora from different time periods. The method achieved 1st place in Sub-task 1 (binary change detection) and 4th place in Sub-task 2 (ranked change detection) at SemEval-2020 Task 1.
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.