Named entity disambiguation at scale

Aghaebrahimian, Ahmad; Cieliebak, Mark

doi:10.1007/978-3-030-58309-5_8

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21530

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Named entity disambiguation at scale
Authors:	Aghaebrahimian, Ahmad Cieliebak, Mark
et. al:	No
DOI:	10.1007/978-3-030-58309-5_8 10.21256/zhaw-21530
Proceedings:	Artificial Neural Networks in Pattern Recognition
Editors of the parent work:	Schilling, Frank-Peter Stadelmann, Thilo
Page(s):	102
Pages to:	110
Conference details:	9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020
Issue Date:	2020
Series:	Lecture Notes in Computer Science
Series volume:	12294
Publisher / Ed. Institution:	Springer
Publisher / Ed. Institution:	Cham
Language:	English
Subjects:	Machine learning; Named entity disambiguation; Alias detection; Deep learning
Subject (DDC):	006: Special computer methods
Abstract:	Named Entity Disambiguation (NED) is a crucial task in many Natural Language Processing applications such as entity linking, record linkage, knowledge base construction, or relation extraction, to name a few. The task in NED is to map textual variations of a named entity to its formal name. It has been shown that parameterless models for NED do not generalize to other domains very well. On the other hand, parametric learning models do not scale well when the number of formal names expands above the order of thousands or more. To tackle this problem, we propose a deep architecture with superior performance on NED and introduce a strategy to scale it to hundreds of thousands of formal names. Our experiments on several datasets for alias detection demonstrate that our system is capable of obtaining superior results with a large margin compared to other state-of-the-art systems.
URI:	https://digitalcollection.zhaw.ch/handle/11475/21530
Fulltext version:	Accepted version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT)
Published as part of the ZHAW project:	AuSuM - Automatic Supply Chain Monitoring
Appears in collections:	Publikationen School of Engineering

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Aghaebrahimian, A., & Cieliebak, M. (2020). Named entity disambiguation at scale [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition (pp. 102–110). Springer. https://doi.org/10.1007/978-3-030-58309-5_8

Aghaebrahimian, A. and Cieliebak, M. (2020) ‘Named entity disambiguation at scale’, in F.-P. Schilling and T. Stadelmann (eds) Artificial Neural Networks in Pattern Recognition. Cham: Springer, pp. 102–110. Available at: https://doi.org/10.1007/978-3-030-58309-5_8.

A. Aghaebrahimian and M. Cieliebak, “Named entity disambiguation at scale,” in Artificial Neural Networks in Pattern Recognition, 2020, pp. 102–110. doi: 10.1007/978-3-030-58309-5_8.

AGHAEBRAHIMIAN, Ahmad und Mark CIELIEBAK, 2020. Named entity disambiguation at scale. In: Frank-Peter SCHILLING und Thilo STADELMANN (Hrsg.), Artificial Neural Networks in Pattern Recognition. Conference paper. Cham: Springer. 2020. S. 102–110

Aghaebrahimian, Ahmad, and Mark Cieliebak. 2020. “Named Entity Disambiguation at Scale.” Conference paper. In Artificial Neural Networks in Pattern Recognition, edited by Frank-Peter Schilling and Thilo Stadelmann, 102–10. Cham: Springer. https://doi.org/10.1007/978-3-030-58309-5_8.

Aghaebrahimian, Ahmad, and Mark Cieliebak. “Named Entity Disambiguation at Scale.” Artificial Neural Networks in Pattern Recognition, edited by Frank-Peter Schilling and Thilo Stadelmann, Springer, 2020, pp. 102–10, https://doi.org/10.1007/978-3-030-58309-5_8.