Effects of pre- And post-processing on type-based embeddings in lexical semantic change detection

Abstract

Lexical semantic change detection is a new and innovative research field. The optimal fine-tuning of models including pre- and postprocessing 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.