DOI QR코드

DOI QR Code

Comparison of Application Effect of Natural Language Processing Techniques for Information Retrieval

정보검색에서 자연어처리 응용효과 분석

  • ;
  • 조영임 (수원대학교 컴퓨터학과)
  • Received : 2012.08.27
  • Accepted : 2012.09.25
  • Published : 2012.11.01

Abstract

In this paper, some applications of natural language processing techniques for information retrieval have been introduced, but the results are known not to be satisfied. In order to find the roles of some classical natural language processing techniques in information retrieval and to find which one is better we compared the effects with the various natural language techniques for information retrieval precision, and the experiment results show that basic natural language processing techniques with small calculated consumption and simple implementation help a small for information retrieval. Senior high complexity of natural language processing techniques with high calculated consumption and low precision can not help the information retrieval precision even harmful to it, so the role of natural language understanding may be larger in the question answering system, automatic abstract and information extraction.

Keywords

References

  1. R. Baeza-Yates, "Challenges in the interaction of information retrieval and natural language processing," Proceedings of 5th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2004, Seoul, Korea, February 15-21, pp. 445-456, 2004.
  2. F. Peng, X. Huang, D. Schuurmans, and N. Cercone, "Investigating the relationship between word segmentation performance and retrieval performance in chinese IR," Proceedings of 19th International Conference on Computational Linguistics, pp. 72-78, 2002.
  3. S. Foo and H. Li, "Chinese word segmentation and its effect on information retrieval," Information Processing and Management, vol. 40, no. 1, pp. 161-191, 2004. https://doi.org/10.1016/S0306-4573(02)00079-1
  4. T. Strzalkowski and B. Vauthey, "Information retrieval using robust natural language processing," Proceedings of the 30th annual meeting on Association for Computational Linguistics, pp. 104-111, 1992.
  5. J Xu and W. B. Croft, "Corpus-based stemming using cooccurrence of word variants," ACM Transactions on Information Systems (TOIS), vol. 16, no. 1, pp. 61-81, 1998. https://doi.org/10.1145/267954.267957
  6. W. Kraaij and R. Pohlmann, "Viewing stemming as recall enhancement," In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,ACM Press, pp. 40-48, 1996.
  7. A. T. Arampatzis, Th. P. van der Weide, C. H. A. Koster, and P. van Bommel, "Text filtering using linguistically-motivated indexing terms," Technical Report CSI-R9901, Computing Science Institute, University of Nijmegen, Nijmegen, The Netherlands, 1999.
  8. Q. Su and H. Zan, "Effects of POS tagging on performance of IR systems," Journal of Chinese Information Processing, vol. 19, no. 2, pp. 58-65, 2005.
  9. T. Brants, "Natural language processing in information retrieval," Proceedings of 20th International Conference on Computational Linguistics, Antwerp, Belgium, pp. 1-13, 2004.
  10. M. Mit ra, C. Buckley, A. Singhal, and C. Cardie, "An analysis of statistical and syntactic phrases," Proceedings of the RIAO97, pp. 200-216, 1997.
  11. S. E. Robert son and S. Walker, "Okapi/ Keenbow at TREC28," Proceedings of the 8th Text Retrieval Conference, NIST Special Publications 500-246, Gaithersburg, pp. 151-162, 1999.
  12. J.-Y. Nie and J ean-Francois Dufort, "Combining words and compound terms for monolingual and cross-language information retrieval," Proceedings of Information, Beijing, pp. 453-458, 2002.
  13. E. M. Voorhees, "Using WordNet to disambiguate word senses for text retrieval," Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, pp. 171-180, 1993.
  14. J. Allan, "Natural language processing for information retrieval," Tutorial Presented at the NAACL/ANLP Language Technology Joint Conference in Seattle,Washington,Apr. 2000.
  15. M. Sanderson, "Word sense disambiguation and information retrieval," Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,ACM Press, pp. 49-57, 1994.
  16. C. Stokoe,M. P. Oakes, and J. Tait, "Word sense disambiguation in information retrieval revisited," Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, pp. 159-166, 2003.
  17. S.-B. Kim, H.-C. Seo, and H.-C. Rim, "Information retrieval usingword senses: root sense tagging approach," Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, pp. 258-265, 2004.
  18. J. Allan and G. Kumaran, "Stemming in the language modeling framework," Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (poster),ACM Press, pp. 455-456, 2003.
  19. C. Wang, M. Zhang, and S. Ma, "A survey of natural language processing in information retrieval," Journal of Chinese Information Processing, vol. 21, no. 2, pp. 40, 2007.
  20. A. F. Smeaton, "Using NLP or NLP resources for information retrieval tasks," In: Natural Language Information Retrieval, T. Strzalkowski, editor, Kluwer, pp. 99-111, 1997.
  21. M. Zhang, R. Song, C. Lin, and S. Ma, "Expansion-based technologies in finding relevant and new information: THU TREC2002 novelty track experiments," Proceedings of the 11th Text Retrieval Conference NIST Special Publication, Gait hersburg, MD, USA, pp. 591-595, 2002.