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GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules

Hu, Xuke and Olimat, Hussein S. Al and Kersten, Jens and Wiegmann, Matti and Klan, Friederike and Sun, Yeran and Fan, Hongchao (2021) GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules. International Journal of Geographical Information Science. Taylor & Francis. doi: 10.1080/13658816.2021.1947507. ISSN 1365-8816.

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Item URL in elib:https://elib.dlr.de/146451/
Document Type:Article
Title:GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Hu, XukeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Olimat, Hussein S. AlTempus Labs IncUNSPECIFIEDUNSPECIFIED
Kersten, JensUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wiegmann, MattiUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Klan, FriederikeUNSPECIFIEDhttps://orcid.org/0000-0002-1856-7334UNSPECIFIED
Sun, YeranSwansea UniversityUNSPECIFIEDUNSPECIFIED
Fan, HongchaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:July 2021
Journal or Publication Title:International Journal of Geographical Information Science
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1080/13658816.2021.1947507
Publisher:Taylor & Francis
ISSN:1365-8816
Status:Published
Keywords:Place name extraction; gazetteer; OpenStreetMap; synthetic data; rule; microblog; deep learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Exploration of citizen science methods, R - QS-Project_04 Big-Data-Plattform
Location: Jena
Institutes and Institutions:Institute of Data Science > Citizen Science
Deposited By: Hu, Xuke
Deposited On:30 Nov 2021 16:00
Last Modified:23 Oct 2023 08:44

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