UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
Proceedings of the Fourteenth Workshop on Semantic Evaluation (2020)
Abstract
In this paper, we describe our method for 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: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely 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.
Authors
BibTex
@inproceedings{prazak-etal-2020-uwb, title = "{UWB} at {S}em{E}val-2020 Task 1: Lexical Semantic Change Detection", author = "Pra{\v{z}}{\'a}k, Ond{\v{r}}ej and P{\v{r}}ib{\'a}{\v{n}}, Pavel and Taylor, Stephen and Sido, Jakub", booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation", month = dec, year = "2020", address = "Barcelona (online)", publisher = "International Committee for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.semeval-1.30", pages = "246--254", abstract = "In this paper, we describe our method for 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: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely 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." }
