DOI QR코드

DOI QR Code

Customized Information Analysis System Using National Defense News Data

국방 기사 데이터를 이용한 맞춤형 정보 분석 시스템

  • Received : 2010.10.07
  • Accepted : 2010.11.29
  • Published : 2010.12.28

Abstract

Customized information analysis system is a software system that can help to extract useful information from non-structured natural language data, process the information to customized form, and provide future forecast and reasoning information. To implement the information analysis system, we need natural language processing technology to analyze natural language, information extraction technology to detect necessary entity and its relationship from text, and data mining technology to discover new and unknown information from extracting data. This paper suggest virtual customized information analysis system processing national defense news data and introduce base technologies for information analysis.

맞춤형 정보 분석 시스템이란 정형화 되어 있지 않은 자연어 텍스트에서 유용한 정보를 추출하고 고객이 요구하는 맞춤형 정보로 가공하여, 미래를 예측하거나 추론하는데 도움을 주는 시스템을 말한다. 이러한 정보 분석 시스템을 구현하기 위해서는 자연어를 분석하는 자연어 처리 기술과 텍스트에서 필요한 개체와 그것들의 관계를 찾아내는 정보 추출 기술, 추출한 데이터로부터 알려지지 않은 새로운 정보를 찾아 내는 데이터 마이닝 기술이 필요하다. 본 논문에서는 국방 기사 데이터를 대상으로 맞춤형 정보 분석을 수행하는 가상의 시스템을 제안하고, 정보 분석을 위한 기반 기술들을 소개한다.

Keywords

References

  1. J. Gimenez and L. Marquez, “Fast and Accurate Part-of-Speech Tagging: The SVM Approach Revisited", In Proceedings of Recent Advances in Natural Language Processing, pp.153-163, 2003.
  2. S. Goldwater and T. Griffiths, “A fully Bayesian approach to unsupervised part-of-speech tagging", In Proceedings of Association for Computational Linguistics, pp.744-751, 2007.
  3. M. Collins, “Head-driven statistical models for natural language parsing", Journal of Association for Computational Linguistics, Vol.29, No.4, pp.589-638, 2003. https://doi.org/10.1162/089120103322753356
  4. J. Nivre, J. Hall, J. Nilsson, A. Chanev, G. Eryigit, S. Kubler, S. Marinov, and E. Marsi, “MaltParser: A language-independent system for data-driven dependency parsing", Journal of Natural Language Engineering, Vol.13, No.2, pp.95-136, 2007.
  5. M. Marneffe, B. Maccartney and C. Manning, “Generating Typed Dependency Parses from Phrase Structure Parses", In Proceedings of International Conference on Language Resources and Evaluation, pp.449-454, 2006.
  6. A. McCallum and W. Li, “Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons", In Proceedings of Human Language Technology - North American Chapter of the Association for Computational Linguistics, pp.188-191, 2003.
  7. R. Evans, “A framework for named entity recognition in the open domain", In Proceedings of Recent Advances in Natural Language Processing, pp.267-276, 2003.
  8. H. Chun, Y. Tsuruoka, J. Kin, R. Shiba, N. Nagata, T. Hishiki, and J. Tsujii, “Extraction of Gene-Disease Relations from Medline Using Domain Dictionaries and Machine Learning", In Pacific Symposium on Biocomputing, pp.4-15, 2006.
  9. E. Hovy, U. Hermjakob and D. a. Ravichandran, “A question/answer typology with surface text patterns", In Proceedings of Human Language Technology, pp.247-251, 2002.
  10. W. J. Frawley, G. Piatetsky-Shapir, and C. J. Matheus, “Knowledge Discovery in Databases: An Overview", AI Magazine, Vol.13, No.3, pp.57-70, 2003.
  11. W. Chu, S. Park, T. Beaupre, N. Motgi, A. Phadke, S. Chakraborty, and J. Zachariah, “A case study of behavior-driven conjoint analysis on Yahoo!: front page today module", In Proceedings of Knowledge Discovery and Data Mining., pp.1097-1104, 2009.
  12. C. D. Manning and H. Schuetze, Foundations of Statistical Natural Language Processing, The MIT Press, 1999.

Cited by

  1. Application and Process Standardization of Terminology Dictionary for Defense Science and Technology vol.11, pp.8, 2011, https://doi.org/10.5392/JKCA.2011.11.8.247