Map-based vs. knowledge-based toponym disambiguation

ABSTRACT

Toponym Disambiguation, i.e. the task of assigning to place name their correct reference in the world, is getting more attention from many researchers. Many methods have been proposed since now, making use of different resources, techniques and sense inventories. Unfortunately, a gold standard for the evaluation of those methods is not yet available; therefore, it is difficult to verify the performance of such methods. Recently, a georeferenced version of WordNet has been developed, a resource that can be used to compare methods that are based on geographical data with methods that use textual information. In this paper we carry out a comparison between two of these methods. The results show that the knowledge-based method allowed us to obtain better results with a smaller context size. On the other hand, we observed that the map-based method needs a large context to obtain a good accuracy.

REFERENCES

• 1: Eneko Agirre , German Rigau, Word sense disambiguation using Conceptual Density, Proceedings of the 16th conference on Computational linguistics, August 05-09, 1996, Copenhagen, Denmark [doi>10.3115/992628.992635]
• 2: Jeffrey Andogah, Gosse Bouma, John Nerbonne, and Erwin Koster. Placename Ambiguity Resolution. In LREC 2008 workshop on Methodologies and Resources for Processing Spatial Language, 2008.
• 3: Davide Buscaldi , Paulo Rosso, A conceptual density-based approach for the disambiguation of toponyms, International Journal of Geographical Information Science, v.22 n.3, p.301-313, January 2008 [doi>10.1080/13658810701626251]
• 4: Davide Buscaldi and Paolo Rosso. Geo-WordNet: Automatic Georeferencing of WordNet. In Proc. 5th Int. Conf. on Language Resources and Evaluation, LREC-2008, 2008.
• 5: Eric Garbin , Inderjeet Mani, Disambiguating toponyms in news, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, p.363-370, October 06-08, 2005, Vancouver, British Columbia, Canada [doi>10.3115/1220575.1220621]
• 6: Jochen L. Leidner. An evaluation dataset for the toponym resolution task. Computers, Environment and Urban Systems, 30(4):400--417, July 2006.
• 7: George A. Miller, WordNet: a lexical database for English, Communications of the ACM, v.38 n.11, p.39-41, Nov. 1995 [doi>10.1145/219717.219748]
• 8: Andreas M. Olligschlaeger and Alexander G. Hauptmann. Multimodal Information Systems and GIS: The Informedia Digital Video Library. In 1999 ESRI User Conference, San Diego, CA, 1999.
• 9: Erik Rauch , Michael Bukatin , Kenneth Baker, A confidence-based framework for disambiguating geographic terms, Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references, p.50-54, May 31, 2003 [doi>10.3115/1119394.1119402]
• 10: David A. Smith , Gregory Crane, Disambiguating Geographic Names in a Historical Digital Library, Proceedings of the 5th European Conference on Research and Advanced Technology for Digital Libraries, p.127-136, September 04-09, 2001
• 11: Rafael Volz, Joachim Kleb, and Wolfgang Mueller. Towards ontology-based disambiguation of geographical identifiers. In 16th International World Wide Web Conference (WWW2007), Banff, Alberta, Canada, 2007.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Language parsing and understanding

General Terms:
Algorithms, Measurement, Performance

Keywords:
conceptual density, toponym disambiguation, word sense disambiguation