@inproceedings{kaiser-etal-2021-effects,
title = "Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection",
author = "Kaiser, Jens and
Kurtyigit, Sinan and
Kotchourko, Serge and
Schlechtweg, Dominik",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.10",
doi = "10.18653/v1/2021.eacl-main.10",
pages = "125--137",
abstract = "Lexical semantic change detection is a new and innovative research field. The optimal fine-tuning of models including pre- and post-processing is largely unclear. We optimize existing models by (i) pre-training on large corpora and refining on diachronic target corpora tackling the notorious small data problem, and (ii) applying post-processing transformations that have been shown to improve performance on synchronic tasks. Our results provide a guide for the application and optimization of lexical semantic change detection models across various learning scenarios.",
}
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%0 Conference Proceedings
%T Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection
%A Kaiser, Jens
%A Kurtyigit, Sinan
%A Kotchourko, Serge
%A Schlechtweg, Dominik
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F kaiser-etal-2021-effects
%X Lexical semantic change detection is a new and innovative research field. The optimal fine-tuning of models including pre- and post-processing is largely unclear. We optimize existing models by (i) pre-training on large corpora and refining on diachronic target corpora tackling the notorious small data problem, and (ii) applying post-processing transformations that have been shown to improve performance on synchronic tasks. Our results provide a guide for the application and optimization of lexical semantic change detection models across various learning scenarios.
%R 10.18653/v1/2021.eacl-main.10
%U https://aclanthology.org/2021.eacl-main.10
%U https://doi.org/10.18653/v1/2021.eacl-main.10
%P 125-137
Markdown (Informal)
[Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection](https://aclanthology.org/2021.eacl-main.10) (Kaiser et al., EACL 2021)
ACL