Relational word embeddings

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 2019. Relational word embeddings. Presented at: 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy, 28 July - 2 August 2019. Published in: Korhonen, A., Traum, D. and Marquez, L. eds. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 3286-3296.

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Abstract

While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding. Such strategies may not be optimal, however, as they are limited by the coverage of available resources and conflate similarity with other forms of relatedness. As an alternative, in this paper we propose to encode relational knowledge in a separate word embedding, which is aimed to be complementary to a given standard word embedding. This relational word embedding is still learned from co-occurrence statistics, and can thus be used even when no external knowledge base is available. Our analysis shows that relational word vectors do indeed capture information that is complementary to what is encoded in standard word embeddings.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Association for Computational Linguistics
Related URLs:	Organisation
Date of First Compliant Deposit:	14 August 2019
Last Modified:	04 Sep 2025 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/124036

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