Lexical Semantic Change Discovery

• URL: http://arxiv.org/abs/2106.03111v1
• Date: Sun, 6 Jun 2021 13:02:38 GMT
• Title: Lexical Semantic Change Discovery
• Authors: Sinan Kurtyigit, Maike Park, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde
• Abstract summary: We propose a shift from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change.
• Score: 22.934650688233734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.

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