MEmbER: Max-Margin Based Embeddings for Entity Retrieval

Jameel, Shoaib, Bouraoui, Zied and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2017. MEmbER: Max-Margin Based Embeddings for Entity Retrieval. Presented at: 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan, 7-11 August 2017. SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: Association for Computing Machinery, pp. 783-792. 10.1145/3077136.3080803

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Abstract

We propose a new class of methods for learning vector space embeddings of entities. While most existing methods focus on modelling similarity, our primary aim is to learn embeddings that are interpretable, in the sense that query terms have a direct geometric representation in the vector space. Intuitively, we want all entities that have some property (i.e. for which a given term is relevant) to be located in some well-defined region of the space. This is achieved by imposing max-margin constraints that are derived from a bagof-words representation of the entities. The resulting vector spaces provide us with a natural vehicle for identifying entities that have a given property (or ranking them according to how much they have the property), and conversely, to describe what a given set of entities have in common. As we show in our experiments, our models lead to a substantially better performance in a range of entity-oriented search tasks, such as list completion and entity ranking.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Association for Computing Machinery
ISBN:	9781450350228
Related URLs:	Organisation
Date of First Compliant Deposit:	11 July 2017
Last Modified:	20 Dec 2022 16:23
URI:	https://orca.cardiff.ac.uk/id/eprint/100910

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