What do deck chairs and sun hats have in common? Uncovering shared properties in large concept vocabularies

Gajbhiye, Amit, Bouraoui, Zied, Li, Na, Chatterjee, Usashi, Espinosa-Anke, Luis ORCID: https://orcid.org/0000-0001-6830-9176 and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2023. What do deck chairs and sun hats have in common? Uncovering shared properties in large concept vocabularies. Presented at: Conference on Empirical Methods in Natural Language Processing, EMNLP, Singapore, 6-10 December 2023. Published in: Bouamor, Houda, Pino, Juan and Bali, Kalika eds. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 10587–10596. 10.18653/v1/2023.emnlp-main.654

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Abstract

Concepts play a central role in many applications. This includes settings where concepts have to be modelled in the absence of sentence context. Previous work has therefore focused on distilling decontextualised concept embeddings from language models. But concepts can be modelled from different perspectives, whereas concept embeddings typically mostly capture taxonomic structure. To address this issue, we propose a strategy for identifying what different concepts, from a potentially large concept vocabulary, have in common with others. We then represent concepts in terms of the properties they share with the other concepts. To demonstrate the practical usefulness of this way of modelling concepts, we consider the task of ultra-fine entity typing, which is a challenging multi-label classification problem. We show that by augmenting the label set with shared properties, we can improve the performance of the state-of-the-art models for this task.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Computer Science & Informatics
Publisher:	Association for Computational Linguistics
ISBN:	979-8-89176-060-8
Date of First Compliant Deposit:	13 February 2024
Date of Acceptance:	7 October 2023
Last Modified:	10 Jun 2024 09:04
URI:	https://orca.cardiff.ac.uk/id/eprint/165643

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