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Spatial characteristics of a large web n-gram corpus

Authors:

Jerome Sautier,

Curdin DerungsAuthors Info & Claims

GIR '15: Proceedings of the 9th Workshop on Geographic Information Retrieval

Article No.: 13, Pages 1 - 2

https://doi.org/10.1145/2837689.2837691

Published: 26 November 2015 Publication History

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Abstract

N-gram corpora, though prominently used to structure and index large natural language corpora, are rarely in the focus of GIR. In this study we describe a step in this direction by characterizing spatial information in a large Web n-gram corpus provided by Microsoft. We explore how continent and country toponyms are represented in this corpus and if basic topological relations can be correctly retrieved. Results suggest that toponym ambiguity has major impact and that although retrieved topological relations are often correct, recall is considerably low. We conclude that further research is required if more fine grained spatial information is to be retrieved from n-grams.

References

[1]

C. Derungs and R. Purves. Where is near? In Proceedings of the 8th International Conference on Geographic Information Science, 2014.

Google Scholar

[2]

M. Graham, B. Hogan, R. K. Straumann, and A. Medhat. Uneven geographies of user-generated information: patterns of increasing informational poverty. Annals of the Association of American Geographers, 104(4):746--764, 2014.

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[3]

J. L. Leidner and M. D. Liberman. Detecting Geographical References in the Form of Place Names and Associated Spatial Natural Language. In R. Purves and C. Jones, editors, Letters on Geographic Information Retrieval, pages 5--12. ACM Sigspatial Special, 2011.

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[4]

C. D. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval, volume 1. Cambridge University Press Cambridge, 2008.

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Google Scholar

[5]

K. Wang, C. Thrasher, E. Viegas, X. Li, and B.-j. P. Hsu. An overview of Microsoft Web N-gram corpus and applications. In Proceedings of the NAACL HLT 2010 Demonstration Session, pages 45--48. Association for Computational Linguistics, 2010.

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Index Terms

Spatial characteristics of a large web n-gram corpus
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems
  1. World Wide Web
    1. Web mining

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Information

Published In

GIR '15: Proceedings of the 9th Workshop on Geographic Information Retrieval

November 2015

90 pages

ISBN:9781450339377

DOI:10.1145/2837689

Editors:
Ross S. Purves
University of Zurich
,
Christopher B. Jones
Cardiff University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2015

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Short-paper

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GIR '15

GIR '15: 9th Workshop on Geographic Information Retrieval

November 26 - 27, 2015

Paris, France

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Overall Acceptance Rate 46 of 61 submissions, 75%

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