Table of Contents
Advances in Artificial Intelligence
Volume 2012, Article ID 562604, 18 pages
Research Article

A Method for Identifying Japanese Shop and Company Names by Spatiotemporal Cleaning of Eccentrically Located Frequently Appearing Words

1Center for Spatial Information Science, The University of Tokyo, Cw-503 Shibasaki Laboratory, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
2Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa City, Chiba 277-8568, Japan

Received 23 July 2011; Revised 9 November 2011; Accepted 2 December 2011

Academic Editor: Mohamed Afify

Copyright © 2012 Yuki Akiyama and Ryosuke Shibasaki. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. Akiyama and R. Shibasaki, “Development of detailed spatio-temporal urban data through the integration of digital maps and yellow page data and feasibility study as complementary data for existing statistical information,” in Proceedings of the Computers in Urban Planning and Urban Management (CUPUM '09), 187, 2009.
  2. Y. Akiyama, T. Shibuki, and R. Shibasaki, “Development of three dimensional monitoring dataset for tenants variations in broad urban area by spatio-temporal integrating digital house maps and yellow page data,” in Proceedings of the 4th International Conference on Intelligent Environments (IE '08), 2008. View at Publisher · View at Google Scholar
  3. T. Ato, K. Omura, T. Arata, and S. Hujii, “The stagnation of commercial accumulation districts in front of the stations in the suburbs of the Tokyo metropolitan area: a study of honatsugi and odawara,” City Planning Review, vol. 41, no. 3, pp. 1037–1042, 2006. View at Google Scholar
  4. H. Ai, Y. Sadahiro, and Y. Asami, “Spatio-temporal analysis of building location and building use in middle scale commercial accumulation districts,” City Planning Review, vol. 43, no. 3, pp. 103–108, 2008. View at Google Scholar
  5. K. Ito and H. Magaribuchi, “Method for making spatio-temporal data from accumulated information: using the identification by resolving geometric and non-geometric ambiguity,” in Proceedings of the Geographic Information Systems Association, vol. 10, pp. 147–150, 2001.
  6. R. Florian, H. Hassan, A. Ittycheriah et al., “A statistical model for multilingual entity detection and tracking,” in Proceedings of the Human Language Technologies Conference (HLT-NAACL '04), pp. 1–8, May 2004.
  7. Q. Tri Tran, T. X. Thao Pham, Q. Hung Ngo, D. Dinh, and N. Collier, “Named entity recognition in Vietnamese documents,” Progress in Informatics, no. 4, pp. 5–13, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. E. F. Tjong Kim Sang and F. D. Meulder, “Introduction to the CoNLL-2003 Shared task: language-independent named entity recognition,” in Proceedings of the 7th Conference on Natural Language Learning (HLT-NAACL '03), vol. 4, pp. 142–147, 2003.
  9. R. Florian, A. Ittycheriah, H. Jing, and T. Zhang, “Named entity recognition through classifier combination,” in Proceedings of the 7th Conference on Natural Language Learning at (HLT-NAACL '03), vol. 4, pp. 168–171, 2003.
  10. H. L. Chieu and H. T. Ng, “Named entity recognition: a maximum entropy approach using global information,” in Proceedings of the 19th International Conference on Computational Linguistics, vol. 1, pp. 1–7, 2002.
  11. R. Steinberger and B. Pouliquen, “Cross-lingual named entity recognition,” Lingvisticae Investigationes, vol. 30, no. 1, pp. 135–162, 2007. View at Google Scholar
  12. T. Bogers, Dutch named entity recognition: optimizing features, algorithms, and output, Ph.D. thesis, University of Van Tilburg, 2004.
  13. C. Sporleder, M. V. Erp, T. Porcelijn, A. V. Bosch, and P. Arntzen, “Identifying named entities in text databases from the natural history domain,” in Proceedings of the 5th International Conference on Language Resources and Evaluation, pp. 1742–1745, 2006.
  14. S. Sato, M. Harada, and K. Kazama, “Measuring similarity among information sources by comparing string frequency distributions,” Information Processing Society of Japan Digital Document, vol. 2002, no. 28, pp. 119–126, 2002. View at Google Scholar
  15. T. Kawakami and H. Suzuki, “A calculation of word similarity using decision list,” IPSJ SIG Technical Report, vol. 2006, no. 94, pp. 85–90, 2006. View at Google Scholar
  16. K. Mishina, S. Tsuchita, S. Kurokawa, and R. Huji, “An emotion similarity calculation using N-gram frequency,” IEICE Technical Report, vol. 107, no. 158, pp. 37–42, 2007, NLC2007-7. View at Google Scholar
  17. D. Cali, A. Condorelli, S. Papa, M. Rata, and L. Zagarella, “Improving intelligence through use of natural language processing. A comparison between NLP interfaces and traditional visual GIS interfaces,” Procedia Computer Science, vol. 5, pp. 920–925, 2011. View at Google Scholar
  18. B. Bitters, “Geospatial reasoning in a natural language processing (NLP) environment,” in Proceedings of the 25th International Cartographic Conference, CO-253, July 2011.
  19. S. Miyagawa, “The Japanese Language,” MIT JP NET, 2011,
  20. D. Klein, J. Smarr, H. Nguyen, and C. D. Manning, “Named entity recognition with character-level models,” in Proceedings of the 7th Conference on Natural Language Learning (HLT-NAACL '03), vol. 4, pp. 180–183, 2003.
  21. S. Kuno, The Structure of the Japanese Language. Current Studies in Linguistics, MIT Press, 1 edition, 1973.
  22. C. E. Shannon, A Mathematical Theory of Communication, University of Illinois Press, 1948.
  23. M. Kondo, An Analysis of Japanese Classical Literature Using Character-Based N-Gram Model, vol. 29, Chiba University, Zinbun Kenkyu, 2000.
  24. T. Odaka, T. Murata, J. Gao et al., “A proposal on student report scoring system using N-gram text analysis method,” Journal of Institute of Electronics, Information, and Communication Engineers, vol. 86, no. 9, pp. 702–705, 2003. View at Google Scholar
  25. J. B. Marino, R. E. Banchs, J. M. Crego et al., “N-gram-based machine translation,” Computational Linguistics, vol. 32, no. 4, pp. 527–549, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. T. Sagara and M. Kitsuregawa, “Cleaning shop names by its location information for shop information retrieval from the web,” Journal of Institute of Electronics, Information, and Communication Engineers, vol. 91, no. 3, pp. 531–537, 2008. View at Google Scholar
  27. Chasen legacy—an old morphological analyzer,