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MEmbER: Max-Margin Based Embeddings for Entity Retrieval

Jameel, Shoaib, Bouraoui, Zied, Schockaert, Steven (2017) MEmbER: Max-Margin Based Embeddings for Entity Retrieval. In: SIGIR '17 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Proceedings of the 40th International ACM SIGIR. . pp. 783-792. ACM ISBN 978-1-4503-5022-8. (doi: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 bag-of-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 (Proceeding)
DOI/Identification number: 10.1145/3077136.3080803
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Shoaib Jameel
Date Deposited: 16 Oct 2018 13:42 UTC
Last Modified: 29 May 2019 21:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69598 (The current URI for this page, for reference purposes)
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