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Geo-tagging news stories using contextual modelling

Geo-tagging news stories using contextual modelling
Geo-tagging news stories using contextual modelling
With the ever-increasing popularity of Location-based Services, geo-tagging a document - the process of identifying geographic locations (toponyms) in the document - has gained much attention in recent years. There have been several approaches proposed in this regard and some of them have reported to achieve higher level of accuracy. The existing geo-tagging approaches perform well at the city or country level, unfortunately, the performance degrades when the same approach is applied to geo-tag at the street/locality level for a specific city. Moreover, these geo-tagging approaches fail completely in the absence of a place mentioned in a document. In this paper, we propose an algorithm to address these two limitations by introducing a model of contexts with respect to a news story. Our algorithm evolves around the idea that a news story can be geo-tagged not only using the place(s) found in the news, but also by geo-tagging certain aspects of its context. An implementation of our proposed approach is presented and its performance is evaluated on a unique data set. Our findings suggest an improvement over existing approaches in street level geo-tagging for a specific city as well as in geo-tagging a news story even when no place is mentioned in it.
Geo-Tagging, Text Mining, Information Retrieval, Contextual Modelling
2155-6377
Ferdous, Md Sadek
1a77c989-cc58-4d52-920a-da9c24f20e7d
Chowdhury, Soumyadeb
243d04a3-126e-4ea1-a47f-29c49c6c545b
Jose, Joemon M.
5f183a8d-8e54-4248-94ff-dfdf046d7592
Ferdous, Md Sadek
1a77c989-cc58-4d52-920a-da9c24f20e7d
Chowdhury, Soumyadeb
243d04a3-126e-4ea1-a47f-29c49c6c545b
Jose, Joemon M.
5f183a8d-8e54-4248-94ff-dfdf046d7592

Ferdous, Md Sadek, Chowdhury, Soumyadeb and Jose, Joemon M. (2017) Geo-tagging news stories using contextual modelling. International Journal of Information Retrieval Research (IJIRR), 7 (4). (doi:10.4018/IJIRR.2017100104).

Record type: Article

Abstract

With the ever-increasing popularity of Location-based Services, geo-tagging a document - the process of identifying geographic locations (toponyms) in the document - has gained much attention in recent years. There have been several approaches proposed in this regard and some of them have reported to achieve higher level of accuracy. The existing geo-tagging approaches perform well at the city or country level, unfortunately, the performance degrades when the same approach is applied to geo-tag at the street/locality level for a specific city. Moreover, these geo-tagging approaches fail completely in the absence of a place mentioned in a document. In this paper, we propose an algorithm to address these two limitations by introducing a model of contexts with respect to a news story. Our algorithm evolves around the idea that a news story can be geo-tagged not only using the place(s) found in the news, but also by geo-tagging certain aspects of its context. An implementation of our proposed approach is presented and its performance is evaluated on a unique data set. Our findings suggest an improvement over existing approaches in street level geo-tagging for a specific city as well as in geo-tagging a news story even when no place is mentioned in it.

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NewsGeoTagging_Revised - Accepted Manuscript
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More information

Accepted/In Press date: 31 July 2016
e-pub ahead of print date: 2017
Keywords: Geo-Tagging, Text Mining, Information Retrieval, Contextual Modelling
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 406419
URI: http://eprints.soton.ac.uk/id/eprint/406419
ISSN: 2155-6377
PURE UUID: 1aeb47ef-cfbc-4430-b08f-a6df7926bd84

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Date deposited: 10 Mar 2017 10:46
Last modified: 05 Jun 2024 18:55

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Contributors

Author: Md Sadek Ferdous
Author: Soumyadeb Chowdhury
Author: Joemon M. Jose

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