정보처리학회논문지:소프트웨어 및 데이터공학 (KIPS Transactions on Software and Data Engineering)

  • 제3권4호
  • /
  • Pages.149-154
  • /
  • 2014
  • /
  • 2287-5905(pISSN)
  • /
  • 2734-0503(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

Q&A 문서의 검색 결과 요약을 활용한 질의응답 시스템

Question and Answering System through Search Result Summarization of Q&A Documents

  • 유동현 (금오공과대학교 컴퓨터공학부) ;
  • 이현아 (금오공과대학교 컴퓨터소프트웨어공학과)
  • 투고 : 2013.11.13
  • 심사 : 2014.02.10
  • 발행 : 2014.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

지식iN과 같은 사용자 참여 질의응답 커뮤니티에서 원하는 질문에 대한 답을 찾기 위해서는 검색 결과로 제공되는 다양한 문서를 일일이 확인하여 판단하는 과정이 필요하다. 만일 사용자가 원하는 답변을 자동으로 정제하여 제시할 수 있다면, 질의응답의 사용성이 크게 향상될 수 있다. 본 논문에서는 질의응답 데이터 분석을 통해 사용자의 질문의 유형을 단어, 목록, 도표, 글의 4가지 유형으로 분류하고, 문서 내 통계적 특성을 활용하여 각 분류별 답변을 자동으로 제시하기 위한 방식을 제안한다. 단어, 목록, 글 유형은 질의어에 대해 검색된 질문을 군집화하고, 군집 내 빈도와 질의어에 대한 근접도, 답변 신뢰도 등으로 계산된 답변 내 어휘의 적합도를 활용하여 요약한 답변을 사용자에게 제시한다. 도표형은 답변들에서 사용자의 의견 정보를 추출하여 의견 통계를 도표로 제시한다.

A user should pick up relevant answers by himself from various search results when using user participation question answering community like Knowledge-iN. If refined answers are automatically provided, usability of question answering community must be improved. This paper divides questions in Q&A documents into 4 types(word, list, graph and text), then proposes summarizing methods for each question type using document statistics. Summarized answers for word, list and text type are obtained by question clustering and calculating scores for words using frequency, proximity and confidence of answers. Answers for graph type is shown by extracting user opinion from answers.

키워드

참고문헌

  1. L. Hirschman, and R. Gaizauskas, "Natural Language Question Answering. The View from Here", Natural Language Engineering, 7:4:275-300 Cambridge University Press, 2001.
  2. Mark T. Maybury, "New Directions in Question Answering", AAAI/MIT Press, 2004.
  3. Rivindu Perera, "IPedagogy: Question answering system based on web information clustering", IEEE 4th International Conference on Technology for Education, pp.245-246, 2012.
  4. Hoa Trans Dang, Diane Kelly and Jimmy Lin, "Overview of the TREC 2007 Question Answering Track", TREC, 2007.
  5. Soyeon Park, Joon Ho Lee, Jiwoon Jeon, "Evaluation of the documents from the Web-based Question and Answer Service", Journal of the Korean society for library and information science, Vol.40, No.1, 2006. https://doi.org/10.4275/KSLIS.2006.40.2.299
  6. Hye-Rhan Chang, Eun-Tae Lee, "Performance Evaluation of the Question and Answer Services in Internet Portals", Journal of information management, Vol.37, No.2, 2006. https://doi.org/10.1633/JIM.2006.37.2.137
  7. Soojung Kim, "Answerers' Strategies to Provide Credible Information in Question Answering Community", Journal of the Korean Society for Information Management, 27(2), 21-35, 2010. https://doi.org/10.3743/KOSIM.2010.27.2.021
  8. Chan-Min Ahn, Bumghi Choi, Seok-Ju Chun, Ju-Hong Lee, Jung-Sik Lee, "Question Recommendation for Knowledge Search System", Journal of the Korean association of information education, Vol.14, No.3, pp.405-416, 2010.
  9. Xueying Jin and Kyung-Soon Lee, "Question Classification Based on Word Association for Question and Answer Archives", The KIPS Transactions: Part B, Vol.17, No.4, pp.327-332, 2010. https://doi.org/10.3745/KIPSTB.2010.17B.4.327
  10. Jongheum Yeon, Junho Shim and Sang-goo Lee, "Modified Naive Bayes Classifier for Categorizing Questions in Question-Answering Community", Journal of KIISE: Computing Practices, Vol.16, No.1, 2010.
  11. E. Agichtein, C. Casillo and D. Donato, "Finding high-quality content in social media", Proc. of the International Conference on Web Search and Web Data Mining, pp.183-194, 2008.
  12. L. A. Adamic, J. Zhang, E. Bakshy and M. S. Ackerman, "Knowledge sharing and yahoo answers: everyone knows something", Proc. of the 17th International Conference on World Wide Web, pp.665-674, 2008.
  13. Hochang Lee, Hyunki Tak and Hyun Ah Lee, "Answer Recommendation for Knowledge Search using Term Frequency", Korea Computer Congress(KCC), Vol.39, No.1, 2012.
  14. Hochang Lee and Hyun Ah Lee, "Answer Suggestion for Knowledge Search", The 24th Annual Conference of Human and Cognitive Language Technology, pp.201-205, 2012.
  15. NAVER Open API, "http://dev.naver.com/openapi"
  16. Machine Learning Lab in The University of Waikato, "Weka", [Online] Available : http://www.cs.waikato.ac.nz/ml
  17. Woo Chul Lee, Hyun Ah Lee and Kong Joo Lee, "Product Evaluation Summarization Through Linguistic Analysis of Product Reviews", The KIPS Transactions: Part B, Vol.17, No.1, 2010. https://doi.org/10.3745/KIPSTB.2010.17B.1.093