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https://doi.org/10.21256/zhaw-21530| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Named entity disambiguation at scale |
| Autor/-in: | Aghaebrahimian, Ahmad Cieliebak, Mark |
| et. al: | No |
| DOI: | 10.1007/978-3-030-58309-5_8 10.21256/zhaw-21530 |
| Tagungsband: | Artificial Neural Networks in Pattern Recognition |
| Herausgeber/-in des übergeordneten Werkes: | Schilling, Frank-Peter Stadelmann, Thilo |
| Seite(n): | 102 |
| Seiten bis: | 110 |
| Angaben zur Konferenz: | 9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020 |
| Erscheinungsdatum: | 2020 |
| Reihe: | Lecture Notes in Computer Science |
| Reihenzählung: | 12294 |
| Verlag / Hrsg. Institution: | Springer |
| Verlag / Hrsg. Institution: | Cham |
| Sprache: | Englisch |
| Schlagwörter: | Machine learning; Named entity disambiguation; Alias detection; Deep learning |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | 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 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Publiziert im Rahmen des ZHAW-Projekts: | AuSuM - Automatic Supply Chain Monitoring |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2020_Aghaebrahimian-Cieliebak_Named-entity-disambiguation-at-scale.pdf | Accepted Version | 141.79 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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.
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