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Location reference recognition from texts: A survey and comparison

Hu, Xuke and Zhou, Zhiyong and Li, Hao and Hu, Yingjie and Gu, Fuqiang and Kersten, Jens and Fan, Hongchao and Klan, Friederike (2022) Location reference recognition from texts: A survey and comparison. ACM Computing Surveys. Association for Computing Machinery (ACM). doi: 10.1145/3625819. ISSN 0360-0300.

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Abstract

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to the process of recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of the specific applications is still missing. Further, there lacks a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and a core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching-based, statistical learning-based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references across the world. Results from this thorough evaluation can help inform future methodological developments for location reference recognition, and can help guide the selection of proper approaches based on application needs.

Item URL in elib:https://elib.dlr.de/188964/
Document Type:Article
Title:Location reference recognition from texts: A survey and comparison
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Hu, XukeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhou, ZhiyongUniversity of ZurichUNSPECIFIEDUNSPECIFIED
Li, HaoUniversity of HeidelbergUNSPECIFIEDUNSPECIFIED
Hu, YingjieUniversity at BuffaloUNSPECIFIEDUNSPECIFIED
Gu, FuqiangChongqing UniversityUNSPECIFIEDUNSPECIFIED
Kersten, JensUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Fan, HongchaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Klan, FriederikeUNSPECIFIEDhttps://orcid.org/0000-0002-1856-7334UNSPECIFIED
Date:2022
Journal or Publication Title:ACM Computing Surveys
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1145/3625819
Publisher:Association for Computing Machinery (ACM)
ISSN:0360-0300
Status:Published
Keywords:geoparsing, location reference recognition, machine learning, comparative review
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 - Environment, Health and Big Data
Location: Jena
Institutes and Institutions:Institute of Data Science > Data Acquisition and Mobilisation
Deposited By: Hu, Xuke
Deposited On:02 Nov 2022 11:01
Last Modified:26 Mar 2024 14:33

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  • Location reference recognition from texts: A survey and comparison. (deposited 02 Nov 2022 11:01) [Currently Displayed]

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