Modelling semantic categories using conceptual neighborhood

Bouraoui, Zied, Camacho Collados, Jose ORCID: https://orcid.org/0000-0003-1618-7239, Espinosa-Anke, Luis ORCID: https://orcid.org/0000-0001-6830-9176 and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2020. Modelling semantic categories using conceptual neighborhood. Presented at: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, NY, USA, 7-12 February 2020. Proceedings of the AAAI Conference on Artificial Intelligence. , vol.34(05) PKP Publishing Services, pp. 7448-7455. 10.1609/aaai.v34i05.6241

Preview

PDF - Accepted Post-Print Version Download (311kB) | Preview

Abstract

While many methods for learning vector space embeddings have been proposed in the field of Natural Language Processing, these methods typically do not distinguish between categories and individuals. Intuitively, if individuals are represented as vectors, we can think of categories as (soft) regions in the embedding space. Unfortunately, meaningful regions can be difficult to estimate, especially since we often have few examples of individuals that belong to a given category. To address this issue, we rely on the fact that different categories are often highly interdependent. In particular, categories often have conceptual neighbors, which are disjoint from but closely related to the given category (e.g. fruit and vegetable). Our hypothesis is that more accurate category representations can be learned by relying on the assumption that the regions representing such conceptual neighbors should be adjacent in the embedding space. We propose a simple method for identifying conceptual neighbors and then show that incorporating these conceptual neighbors indeed leads to more accurate region based representations.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	PKP Publishing Services
ISBN:	978-1-57735-835-0
ISSN:	2159-5399
Related URLs:	Publisher Organisation
Date of First Compliant Deposit:	10 January 2020
Date of Acceptance:	3 December 2019
Last Modified:	04 Sep 2025 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/127432

Citation Data

Cited 6 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item