Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

MEmbER: Max-Margin Based Embeddings for Entity Retrieval

Jameel, Shoaib, Bouraoui, Zied and Schockaert, Steven 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. 10.1145/3077136.3080803

[img]
Preview
PDF - Accepted Post-Print Version
Download (733kB) | Preview

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: Completion
Status: In Press
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Related URLs:
Date of First Compliant Deposit: 11 July 2017
Last Modified: 12 Jun 2019 03:04
URI: http://orca.cf.ac.uk/id/eprint/100910

Citation Data

Cited 13 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics