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MEmbER: Max-Margin Based Embeddings for Entity Retrieval

Jameel, Shoaib and Bouraoui, Zied and Schockaert, Steven (2017) MEmbER: Max-Margin Based Embeddings for Entity Retrieval. In: SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval, 2017-08-07 - 2017-08-11, Tokyo.

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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)
Additional Information: Published proceedings: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 17 Aug 2020 16:17
Last Modified: 23 Sep 2022 19:37

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