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Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic Change Modeling

arXiv 2408.05184 ↗First posted 9 Aug 2024cs.CLcited by 0

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Abstract (arXiv)

This paper describes our solution of the first subtask from the AXOLOTL-24 shared task on Semantic Change Modeling. The goal of this subtask is to distribute a given set of usages of a polysemous word from a newer time period between senses of this word from an older time period and clusters representing gained senses of this word. We propose and experiment with three new methods solving this task. Our methods achieve SOTA results according to both official metrics of the first substask. Additionally, we develop a model that can tell if a given word usage is not described by any of the provided sense definitions. This model serves as a component in one of our methods, but can potentially be useful on its own.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 9 Aug 2024.

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References · 18

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  11. 2022Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddingscited by 18
  12. 2022Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitutioncited by 12
6 more
  1. 2024Graph-based Clustering for Detecting Semantic Change Across Time and Languagescited by 10
  2. 2020CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representationscited by 9
  3. 2020UWB at SemEval-2020 Task 1: Lexical Semantic Change Detectioncited by 7
  4. 2024Detection of Non-recorded Word Senses in English and Swedishcited by 3
  5. 2024AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modelingcited by 3
  6. 2024Multilingual Substitution-based Word Sense Inductioncited by 1

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