Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Hybrid Method for classifying User's Asking Points

하이브리드 방법의 사용자 질의 의도 분류

  • Published : 2003.02.01
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Abstract

For QA systems to return correct answer phrases, it is very important that they correctly and stably analyze users' intention. To satisfy this need, we propose a question type classifier (i.e. asking point identifier) for practical QA systems. The classifier uses a hybrid method that combines a statistical method with a rule-based method according to some heuristic rules. Owing to the hybrid method, the classifier can reduce the time to manually construct rules, yield high precision rate and guarantee robustness. In the experiment, we accomplished 80% accuracy of the question type classification.

질의응답 시스템이 올바른 답변을 제시하기 위해서는 사용자의 의도를 정확하고 강건하게 파악하는 것이 매우 중요하다. 이러한 요구 사항을 만족시키기 위해서 본 논문에서는 실용적 실의응답 시스템을 위한 질의 유형 분류기를 제안한다 제안된 실의 유형 분류기는 규칙 기반의 방법과 통계 기반의 방법을 접목시킨 하이브리드 방법을 사용한다. 제안된 방법을 사용함으로써 수동으로 규칙을 작성하는 시간을 줄일 수 있었고 정확률을 향상시킬 수 있었으며 안정성을 보장받을 수 있었다 제안된 방법에 대한 실험에서 질의 유형을 분류하는데 80%의 정확률을 얻었다.

Keywords

References

  1. Clarke C. L. A., Cormack G. V., Kisman D. I. E. and Lynam T. R., 'Question Answering by Passage Selection (MultiText Experments for TREC-9.' In Proceedings of the Ninth Text REtrieval Conference, Gaithersburg, Maryland, 2000
  2. Iittycheriah A., Franz M., Zhu W. and Ratnaparkhi A.m 'IBM's Statistical Question Answering System.' In Proceedings of the Ninth Text REtrieval Conference, Gaithersburg, Maryland, 2000
  3. Moldovan D., Harabagiu S., Pasca M., Mihalcca R., Goodrum R., Girju R. and Rus V., 'LASSO-A tool for Surfing the Answer Net.' In Proceedings of the Eighth Text REtrieval Conference, Gaithersburg, Maryland
  4. Ferret O., Grau B., Illouz G. and Jacquemin C., 'QALC the Question Answering program of the Language and Cognition group at LIMSI CNRS.' In Proceedings of the Eighth Text REtrieval Conference, Gaithersburg, Maryland, 1999
  5. Hull D.A., 'Xerox TREC-8 Question Answering Track Report.' In Proceedings of the Eighth Text REtrieval Conference, Gaithersburg, Maryland, 1999
  6. Prager J., Radev D., Brown E. and Coden A., 'The Use of Predictive Annotation for Question Answering in TREC8.' In Proceedings of the Eighth Text REtrieval Conference, Gaithersburg, Maryland, 1999
  7. Srihari R., and Li W., 'Information Extraction Supported Question Answering,' In Proceedings of the Eighth Text REtrieval Conference, Gaithersburg, Matyland, 1999
  8. Kupiec J., 'MURAX: A Robust Linguistic Approach for Question Answering Using an On line Encyclopedia.' In Proceedings of SIGIR'93, 1993
  9. Quinlan R. J., C4.5: Programs for Machine Learning, Morgan Kaufmann, San Matco, CA, 1993
  10. Ittycheriah A., Franz M., Zhu W. and Ratnaparkhi A., 'Question Answering Using Maximum Entropy Components.' In Proceedings of NAACL, 2001
  11. Hermjakob U., 'Parsing and Question Classification Classification for Question Answering.' In Proceedings of the ACL Workshop Open-Domain Question Answering, pp.17-22, 2001
  12. Mann G. S., 'A Statistical Method for Short Answer Extraction.' In Proceedings of the ACL Workshop Open-Domain Question Answering, pp.13-30, 2001
  13. Miller G., 'WordNet: An on line lexical database.' International Journal of Lexicography, Vol. 3(4), 1990
  14. diquest, http://www.diquest.com
  15. Kim H. and Seo J., 'Automatic Extraction of a Syntactic Pattern for an Analysis of Speech Act: A Neural Network Model.' In Proceedings of ICONIP-2000, Korea, 2000
  16. Kim, H., Cho J. and Seo J., 'Fuzzy Trigram Model for Speech Act Analysis of Utterances in Dialogues.' In Proceedings of Conference on FUZZ-IEEE99, Seoul, Korea, 1999