Is This Statement About A Place? Comparing two perspectives (Short Paper)

Authors Alan M. MacEachren, Richard Caneba, Hanzhou Chen, Harrison Cole, Emily Domanico, Nicholas Triozzi, Fangcao Xu, Liping Yang



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Author Details

Alan M. MacEachren
  • GeoVISTA Center, Dept. of Geography, Penn State University, University Park, PA, USA
Richard Caneba
  • College of Information Science & Technology, Penn State University, University Park, PA, USA
Hanzhou Chen
  • Department of Geography, Penn State University, University Park, PA 16802, USA
Harrison Cole
  • Department of Geography, Penn State University, University Park, PA 16802, USA
Emily Domanico
  • Department of Geography, Penn State University, University Park, PA 16802, USA
Nicholas Triozzi
  • Dept. of Anthropology, Penn State University, University Park, PA 16802, USA
Fangcao Xu
  • Department of Geography, Penn State University, University Park, PA 16802, USA
Liping Yang
  • Department of Geography, Penn State University, University Park, PA 16802, USA

Cite AsGet BibTex

Alan M. MacEachren, Richard Caneba, Hanzhou Chen, Harrison Cole, Emily Domanico, Nicholas Triozzi, Fangcao Xu, and Liping Yang. Is This Statement About A Place? Comparing two perspectives (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 44:1-44:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.44

Abstract

Text often includes references to places by name; in prior work, more than 20% of a sample of event-related tweets were found to include place names. Research has addressed the challenge of leveraging the geographic data reflected in text statements, with well-developed methods to recognize location mentions in text and related work on automated toponym resolution (deciding which place in the world is meant by a place name). A core issue that remains is to distinguish between text that mentions a place or places and text that is about a place or places. This paper presents the first step in research to address this challenge. The research reported here sets the conceptual and practical groundwork for subsequent supervised machine learning research; that research will leverage human-produced training data, for which a judgment is made about whether a statement is or is not about a place (or places), to train computational methods to do this classification for large volumes of text. The research step presented here focuses on three questions: (1) what kinds of entities are typically conceptualized as places, (2) what features of a statement prompt the reader to judge a statement to be about a place (or not about a place) and (3) how do judgments of whether or not a statement is about a place compare between a group of experts who have studied the concept of "place" from a geographic perspective and a cross-section of individuals recruited through a crowdsourcing platform to make these judgments.

Subject Classification

ACM Subject Classification
  • Information systems → Information retrieval
  • Computing methodologies → Natural language processing
Keywords
  • geographic information retrieval
  • spatial language
  • crowdsourcing

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