@inproceedings{chen-etal-2023-chiwug-graph,
title = "{C}hi{WUG}: A Graph-based Evaluation Dataset for {C}hinese Lexical Semantic Change Detection",
author = "Chen, Jing and
Chersoni, Emmanuele and
Schlechtweg, Dominik and
Prokic, Jelena and
Huang, Chu-Ren",
editor = "Tahmasebi, Nina and
Montariol, Syrielle and
Dubossarsky, Haim and
Kutuzov, Andrey and
Hengchen, Simon and
Alfter, David and
Periti, Francesco and
Cassotti, Pierluigi",
booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.lchange-1.10",
doi = "10.18653/v1/2023.lchange-1.10",
pages = "93--99",
abstract = "Recent studies suggested that language models are efficient tools for measuring lexical semantic change. In our paper, we present the compilation of the first graph-based evaluation dataset for lexical semantic change in the context of the Chinese language, specifically covering the periods of pre- and post- Reform and Opening Up. Exploiting the existing framework DURel, we collect over 61,000 human semantic relatedness judgments for 40 targets. The inferred word usage graphs and semantic change scores provide a basis for visualization and evaluation of semantic change.",
}
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%0 Conference Proceedings
%T ChiWUG: A Graph-based Evaluation Dataset for Chinese Lexical Semantic Change Detection
%A Chen, Jing
%A Chersoni, Emmanuele
%A Schlechtweg, Dominik
%A Prokic, Jelena
%A Huang, Chu-Ren
%Y Tahmasebi, Nina
%Y Montariol, Syrielle
%Y Dubossarsky, Haim
%Y Kutuzov, Andrey
%Y Hengchen, Simon
%Y Alfter, David
%Y Periti, Francesco
%Y Cassotti, Pierluigi
%S Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chen-etal-2023-chiwug-graph
%X Recent studies suggested that language models are efficient tools for measuring lexical semantic change. In our paper, we present the compilation of the first graph-based evaluation dataset for lexical semantic change in the context of the Chinese language, specifically covering the periods of pre- and post- Reform and Opening Up. Exploiting the existing framework DURel, we collect over 61,000 human semantic relatedness judgments for 40 targets. The inferred word usage graphs and semantic change scores provide a basis for visualization and evaluation of semantic change.
%R 10.18653/v1/2023.lchange-1.10
%U https://aclanthology.org/2023.lchange-1.10
%U https://doi.org/10.18653/v1/2023.lchange-1.10
%P 93-99
Markdown (Informal)
[ChiWUG: A Graph-based Evaluation Dataset for Chinese Lexical Semantic Change Detection](https://aclanthology.org/2023.lchange-1.10) (Chen et al., LChange 2023)
ACL