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.
@article{arxiv.2101.09368,
title = {Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection},
author = {Jens Kaiser and Sinan Kurtyigit and Serge Kotchourko and Dominik Schlechtweg},
journal= {arXiv preprint arXiv:2101.09368},
year = {2021}
}