research-article

A metadata geoparsing system for place name recognition and resolution in metadata records

Authors:

José Borbinha,

Bruno MartinsAuthors Info & Claims

JCDL '11: Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries

Pages 339 - 348

https://doi.org/10.1145/1998076.1998140

Published: 13 June 2011 Publication History

Abstract

This paper describes an approach for performing recognition and resolution of place names mentioned over the descriptive metadata records of typical digital libraries. Our approach exploits evidence provided by the existing structured attributes within the metadata records to support the place name recognition and resolution, in order to achieve better results than by just using lexical evidence from the textual values of these attributes. In metadata records, lexical evidence is very often insufficient for this task, since short sentences and simple expressions are predominant. Our implementation uses a dictionary based technique for recognition of place names (with names provided by Geonames), and machine learning for reasoning on the evidences and choosing a possible resolution candidate. The evaluation of our approach was performed in data sets with a metadata schema rich in Dublin Core elements. Two evaluation methods were used. First, we used cross-validation, which showed that our solution is able to achieve a very high precision of 0,99 at 0,55 recall, or a recall of 0,79 at 0,86 precision. Second, we used a comparative evaluation with an existing commercial service, where our solution performed better on any confidence level (p<0,001).

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Cited By

Zhang ZBethard S(2024)A survey on geocoding: algorithms and datasets for toponym resolutionLanguage Resources and Evaluation10.1007/s10579-024-09730-2Online publication date: 10-Jun-2024
https://doi.org/10.1007/s10579-024-09730-2
Hu XZhou ZLi HHu YGu FKersten JFan HKlan F(2023)Location Reference Recognition from Texts: A Survey and ComparisonACM Computing Surveys10.1145/362581956:5(1-37)Online publication date: 27-Nov-2023
https://dl.acm.org/doi/10.1145/3625819
Hassini NMahmoudi KFaiz S(2023)A Hybrid Approach for Spatial Information Extraction from Natural Language Text2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA59173.2023.10479267(1-8)Online publication date: 4-Dec-2023
https://doi.org/10.1109/AICCSA59173.2023.10479267
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Index Terms

A metadata geoparsing system for place name recognition and resolution in metadata records
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information storage systems
    1. Record storage systems

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cover image ACM Conferences

JCDL '11: Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries

June 2011

500 pages

ISBN:9781450307444

DOI:10.1145/1998076

General Chair:
Glen Newton
Carleton University, Canada
,
Program Chairs:
Michael Wright
UCAR/NCAR, USA
,
Lillian Cassel
Villanova University, USA

Copyright © 2011 ACM.

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Published: 13 June 2011

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JCDL '11

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JCDL '11: Joint Conference on Digital Libraries

June 13 - 17, 2011

Ontario, Ottawa, Canada

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Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Cited By

Zhang ZBethard S(2024)A survey on geocoding: algorithms and datasets for toponym resolutionLanguage Resources and Evaluation10.1007/s10579-024-09730-2Online publication date: 10-Jun-2024
https://doi.org/10.1007/s10579-024-09730-2
Hu XZhou ZLi HHu YGu FKersten JFan HKlan F(2023)Location Reference Recognition from Texts: A Survey and ComparisonACM Computing Surveys10.1145/362581956:5(1-37)Online publication date: 27-Nov-2023
https://dl.acm.org/doi/10.1145/3625819
Hassini NMahmoudi KFaiz S(2023)A Hybrid Approach for Spatial Information Extraction from Natural Language Text2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA59173.2023.10479267(1-8)Online publication date: 4-Dec-2023
https://doi.org/10.1109/AICCSA59173.2023.10479267
Hu YMai GCundy CChoi KLao NLiu WLakhanpal GZhou RJoseph K(2023)Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messagesInternational Journal of Geographical Information Science10.1080/13658816.2023.226649537:11(2289-2318)Online publication date: 9-Oct-2023
https://doi.org/10.1080/13658816.2023.2266495
Tao LXie ZXu DMa KQiu QPan SHuang B(2022)Geographic Named Entity Recognition by Employing Natural Language Processing and an Improved BERT ModelISPRS International Journal of Geo-Information10.3390/ijgi1112059811:12(598)Online publication date: 28-Nov-2022
https://doi.org/10.3390/ijgi11120598
Cardoso AMartins BEstima J(2021)A Novel Deep Learning Approach Using Contextual Embeddings for Toponym ResolutionISPRS International Journal of Geo-Information10.3390/ijgi1101002811:1(28)Online publication date: 31-Dec-2021
https://doi.org/10.3390/ijgi11010028
Hu YGui ZWang JLi M(2021)Enriching the metadata of map images: a deep learning approach with GIS-based data augmentationInternational Journal of Geographical Information Science10.1080/13658816.2021.196840736:4(799-821)Online publication date: 23-Aug-2021
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Wang JHu YJoseph K(2020)NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messagesTransactions in GIS10.1111/tgis.1262724:3(719-735)Online publication date: 14-May-2020
https://doi.org/10.1111/tgis.12627
Wang RHu YZhou ZYang K(2020)Tracking Flooding Phase Transitions and Establishing a Passive Hotline With AI-Enabled Social Media DataIEEE Access10.1109/ACCESS.2020.29941878(103395-103404)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2994187
Hu YAdams B(2020)Harvesting Big Geospatial Data from Natural Language TextsHandbook of Big Geospatial Data10.1007/978-3-030-55462-0_19(487-507)Online publication date: 17-Dec-2020
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