Alshaikh, Rana, Bouraoui, Zied and Schockaert, Steven  ORCID: https://orcid.org/0000-0002-9256-2881
2019.
Learning conceptual spaces with disentangled facets.
Presented at: CoNLL 2019: The SIGNLL Conference on Computational Natural Language Learning,
Hong Kong, China,
3-4 November 2019.
Published in: Bansal, Mohit and Villavicencio, Aline eds.
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL).
Hong Kong, China:
Association for Computational Linguistics,
pp. 131-139.
10.18653/v1/K19-1013
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Abstract
Conceptual spaces are geometric representations of meaning that were proposed by G ̈ardenfors (2000). They share many similarities with the vector space embeddings that are commonly used in natural language processing. However, rather than representing entities in a single vector space, conceptual spaces are usually decomposed into several facets, each of which is then modelled as a relatively low dimensional vector space. Unfortunately, the problem of learning such conceptual spaces has thus far only received limited attention. To address this gap, we analyze how, and to what extent, a given vector space embedding can be decomposed into meaningful facets in an unsupervised fashion. While this problem is highly challenging, we show that useful facets can be discovered by relying on word embeddings to group semantically related features.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Status: | Published |
| Schools: | Professional Services > Advanced Research Computing @ Cardiff (ARCCA) Schools > Computer Science & Informatics |
| Publisher: | Association for Computational Linguistics |
| ISBN: | 978-195073772-7 |
| Date of First Compliant Deposit: | 9 October 2019 |
| Last Modified: | 28 Apr 2026 09:51 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/125927 |
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