19/04/2021

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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