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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 Al-Olimat, Hussein 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. ISSN 1365-8816. (Submitted)

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Abstract

Place name extraction refers to the task of detecting precise location information in texts like microblogs. It is a vital task to assist disaster response, revealing where the damages are, where people need assistance, and where help can be found. All current approaches for extracting the place names from microblogs face crucial problems: rule-based methods do not generalize, gazetteer-based methods do not detect unknown multi-word place names, and machine learning methods lack sufficient data, which is costly to annotate on scale. We propose a hybrid method that avoids these problems, named GazPNE, which fuses rules, gazetteers, and deep learning methods to achieve state-of-the-art-performance without requiring any manually annotated data. Specifically, we utilize C-LSTM, a fusion of Convolutional and Long Short-Term Memory Neural Networks, to decide if an n-gram in a microblog text is a place name or not. The C-LSTM is trained on 4.6 million positive examples extracted from OpenStreetMap and GeoNames and 220 million negative examples synthesized by rules and evaluated on 4,500 disaster-related tweets, including 9,026 place names from three floods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). Our method improves the previous state-of-the-art by 6%, achieving an F1 of 0.86.

Item URL in elib:https://elib.dlr.de/140291/
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 iD
Hu, XukeXuke.Hu (at) dlr.deUNSPECIFIED
Al-Olimat, HusseinTempus Labs IncUNSPECIFIED
Kersten, JensJens.Kersten (at) dlr.deUNSPECIFIED
Wiegmann, MattiMatti.Wiegmann (at) dlr.deUNSPECIFIED
Klan, FriederikeFriederike.Klan (at) dlr.dehttps://orcid.org/0000-0002-1856-7334
Sun, YeranSwansea UniversityUNSPECIFIED
Fan, Hongchaohongchao.fan (at) ntnu.noUNSPECIFIED
Date: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
Publisher:Taylor & Francis
ISSN:1365-8816
Status:Submitted
Keywords:Place name extraction; Gazetteer; OpenStreetMap; Synthetic data; Rule;Microblog; Annotation free; Deep learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Jena
Institutes and Institutions:Institute of Data Science
Institute of Data Science > Citizen Science
Deposited By: Hu, Xuke
Deposited On:14 Jan 2021 13:18
Last Modified:14 Jan 2021 13:18

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