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Characterization of toponym usages in texts

Published:04 November 2014Publication History

ABSTRACT

Toponyms in texts and search queries are often used figuratively and do not directly refer to the locations they reference in their literal sense. Different usage kinds and stylistic devices characterize toponym usages in texts. It is thus crucial for a Geographic Information Retrieval (GIR) system to precisely distinguish these different toponym usages at indexing and at query time in order to best address a given information need and the geospatial footprint of a document.

For that purpose, we analyze which of the classic stylistic devices such as allegories, metaphors, or metonymies are used together with toponyms. We use these categories as a foundation for a systematic approach towards the characterization of toponym usages in texts which we believe is necessary to further boost retrieval effectiveness of future GIR systems. A prototype implements this characterization exemplary for texts written in German. We evaluate the effectiveness of our approach against a reference corpus to show the general feasibility. Our approach provides a basis for a wide range of more sophisticated applications such as for example text genre detection.

References

  1. D. Bamman, B. O'Connor, and N. A. Smith. Learning Latent Personas of Film Characters. In Proc. of the 51st Annual Meeting of the Association for Computational Linguistics, pages 352--361, Sofia, Bulgaria, 2013. ACL.Google ScholarGoogle Scholar
  2. D. Buscaldi and P. Rosso. A conceptual density-based approach for the disambiguation of toponyms. Intl. Journal of Geographical Information Science, 22(3):301--313, Mar. 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. H. Cunningham and D. Maynard. GATE: an architecture for development of robust HLT applications. In Proc. of the 40th Annual Meeting of the Association for Computational Linguistics, pages 168--175, Philadelphia, PA, USA, 2002. ACL. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Finkel, T. Grenager, and C. Manning. Incorporating non-local information into information extraction systems by Gibbs sampling. In Proc. of the 43nd Annual Meeting of the Association for Computational Linguistics, pages 363--370, Ann Arbor, MI, USA, 2005. ACL. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Gawryjolek, C. Dimarco, and R. Harris. An Annotation Tool for Automatically Detecting Rhetorical Figures. In Proc. of the IJCAI-09 Workshop on Computational Models of Natural Argument, http://www.cmna.info/CMNA9/proceedings/CMNA9-Gawryjolek%20et%20al.pdf, last visit: 25.8.14, Pasadena, CA, USA, 2009.Google ScholarGoogle Scholar
  6. B. Hamp and H. Feldweg. GermaNet - A Lexical-Semantic Net for German. In Proc. of ACL Workshop Automatic Information Extraction and Building of Lexical Semantic Resources for NLP Applications, pages 9--15, Madrid, Spain, 1997.Google ScholarGoogle Scholar
  7. A. Henrich, V. Lüdecke, and D. Blank. Approaches for determining the geographic footprint of arbitrary terms for retrieval and visualization. In Proceedings of the 16th ACM SIGSPATIAL Intl. Conf. on Advances in Geographic Information Systems, GIS '08, pages 1--4, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. R. Kelly, N. A. Abbott, R. A. Harris, C. DiMarco, and D. R. Cheriton. Toward an ontology of rhetorical figures. In Proc. of the 28th Intl. Conf. on Design of Communication, pages 123--130, Sao Carlos, Sao Paulo, Brazil, 2010. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. Kolmer and C. Rob-Santer. Textbook Rhetoric (in German). Verlag Ferdinand Schöningh, Paderborn, 2002.Google ScholarGoogle Scholar
  10. J. Leveling and S. Hartrumpf. On metonymy recognition for geographic information retrieval. Intl. Journal of Geographical Information Science, 22(3):289--299, Mar. 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. Marcu. The rhetorical parsing of natural language texts. In Proc. of the 35th Annual Meeting of the Association for Computational Linguistics, pages 96--103, Madrid, Spain, 1997. ACL. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. K. Markert and U. Hahn. Understanding metonymies in discourse. Artificial Intelligence, 135(1-2):145--198, Feb. 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. K. Markert and M. Nissim. Data and models for metonymy resolution. Language Resources and Evaluation, 43(2):123--138, Feb. 2009.Google ScholarGoogle ScholarCross RefCross Ref
  14. V. Nastase, A. Judea, K. Markert, and M. Strube. Local and global context for supervised and unsupervised metonymy resolution. In Proc. of the Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 183--193, Jeju Island, Korea, 2012. ACL. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. H. P. Nii. Blackboard Systems. Technical report, Stanford University, CA, USA, CS-TR-86-1123, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. M. Nissim and K. Markert. Syntactic features and word similarity for supervised metonymy resolution. In Proc. of the 41st Annual Meeting of the Association for Computational Linguistics, pages 56--63, Sapporo, Japan, 2003. ACL. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. P. Perera and R. Witte. A Self-Learning Context-Aware Lemmatizer for German. In Proc. of the Conf. on Human Language Technology and Empirical Methods in Natural Language Processing, pages 636--643, Vancouver, BC, Canada, 2005. ACL. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. H. Schmid. Probabilistic part-of-speech tagging using decision trees. In Proc. of the Intl. Conf. on New Methods in Language Processing, Manchester, UK, 1994.Google ScholarGoogle Scholar
  19. H. Schmid. Improvements in part-of-speech tagging with an application to German. In Proc. of the SIGDAT-Workshop, Dublin, Ireland, 1995. ACL.Google ScholarGoogle Scholar
  20. R. Sennrich, G. Schneider, M. Volk, and M. Warin. A new hybrid dependency parser for German. In Proc. of the Biannual Meeting of the German Society for Computational Linguistics and Language Technology, pages 115--124, Potsdam, Germany, 2009. GSCL.Google ScholarGoogle Scholar

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            cover image ACM Conferences
            GIR '14: Proceedings of the 8th Workshop on Geographic Information Retrieval
            November 2014
            94 pages
            ISBN:9781450331357
            DOI:10.1145/2675354

            Copyright © 2014 ACM

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            Publication History

            • Published: 4 November 2014

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            GIR '14 Paper Acceptance Rate11of15submissions,73%Overall Acceptance Rate46of61submissions,75%

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