Published on Wed Jul 22 2020

SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, Nina Tahmasebi

Lexical Semantic Change detection is a very active research area, with applications in NLP, lexicography, and linguistics. No gold standards are available to the community, which hinders progress. 33 teams submitted 186 systems, which were evaluated on two subtasks.

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Abstract

Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.

Mon Nov 30 2020
NLP
UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
This paper describes a method for the detection of lexical semantic change. We examine semantic differences between words in two corpora, chosen from different time periods. Our method is fully unsupervised and language independent.
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Tue Jan 21 2020
NLP
Shared task: Lexical semantic change detection in German (Student Project Report)
We present the results of the first shared task on unsupervised. semantic change detection (LSCD) in German based on the evaluation framework proposed by Schlechtweg et al. (2019)
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Fri Nov 06 2020
NLP
OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection
We exploit one of the earliest. most influential semantic change detection models based on Skip-Gram with.Negative Sampling, Orthogonal Procrustes alignment and Cosine Distance. We obtain the winning submission of the shared task with near to perfect accuracy.
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Sun Jun 06 2021
NLP
Lexical Semantic Change Discovery
Change Detection is a field of Lexical Semantic research. We propose a shift of focus from change detection to change discovery. We provide an almost fully automated framework for evaluation and discovery.
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Tue Oct 13 2020
NLP
RuSemShift: a dataset of historical lexical semantic change in Russian
RuSemShift is a large-scale manually annotated test set for the task of semantic change modeling in Russian. Target words were annotated by multiple crowd-source workers.
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Wed Dec 02 2020
Artificial Intelligence
SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
This paper describes SChME (Semantic Change Detection with Model Ensemble), a method used in SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. The method uses signals of distributional models and wordfrequency models.
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