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Detecting the Geospatialness of Prepositions from Natural Language Text (Short Paper)

Authors Mansi Radke , Prarthana Das, Kristin Stock , Christopher B. Jones

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Mansi Radke
  • Computer Science and Engineering Department, Visvesvaraya National Institute of Technology, Nagpur, India
Prarthana Das
  • Computer Science and Engineering Department, Visvesvaraya National Institute of Technology, Nagpur, India
Kristin Stock
  • Massey Geoinformatics Collaboratory, Massey University, Auckland, New Zealand
Christopher B. Jones
  • School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

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Mansi Radke, Prarthana Das, Kristin Stock, and Christopher B. Jones. Detecting the Geospatialness of Prepositions from Natural Language Text (Short Paper). In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 11:1-11:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


There is increasing interest in detecting the presence of geospatial locative expressions that include spatial relation terms such as near or within <some distance>. Being able to do so provides a foundation for interpreting relative descriptions of location and for building corpora that facilitate the development of methods for spatial relation extraction and interpretation. Here we evaluate the use of a spatial role labelling procedure to distinguish geospatial uses of prepositions from other spatial and non-spatial uses and experiment with the use of additional machine learning features to improve the quality of detection of geospatial prepositions. An annotated corpus of nearly 2000 instances of preposition usage was created for training and testing the classifiers.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Natural language processing
  • spatial language
  • natural language processing
  • geospatial language


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