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

• URL: http://arxiv.org/abs/2101.09368v2
• Date: Tue, 26 Jan 2021 19:32:24 GMT
• Title: Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection
• Authors: Jens Kaiser, Sinan Kurtyigit, Serge Kotchourko, Dominik Schlechtweg
• Abstract summary: We optimize existing models by (i) pre-training on large corpora and refining on diachronic target corpora tackling the notorious small data problem. Our results provide a guide for the application and optimization of lexical semantic change detection models across various learning scenarios.
• Score: 4.7677261488999205
• License: http://creativecommons.org/licenses/by/4.0/
• 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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