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arXiv · 2101.09368

Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection

Jens Kaiser·Sinan Kurtyigit·Serge Kotchourko·Dominik Schlechtweg
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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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Jens Kaiser, Sinan Kurtyigit, Serge Kotchourko, Dominik Schlechtweg. 2021-01-22. Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection. https://arxiv.org/abs/2101.09368

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