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ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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  • P-ISSN1013-0799
  • E-ISSN2586-2073
  • KCI

아이디어 마이닝 분야에서 문헌과 웹페이지의 아이디어 발췌에 대한 연구

A Study on Extracting Ideas from Documents and Webpages in the Field of Idea Mining

정보관리학회지 / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2012, v.29 no.1, pp.25-43
https://doi.org/10.3743/KOSIM.2012.29.1.025
이태영 (전북대학교)

Abstract

The ideas and quasi-ideas useful for human's creation were drawn out from documents and webpages with extraction methods used in idea mining, opinion mining, and topic signal mining. The extraction methods comprised (1) decisive cue phrases, (2) cue figures and sounds, (3) contextual signals, and (4) discourse segmentations, They tested on the idea samples, such as thoughts, plans, opinions, writings, figures, sounds, and formulas. Methods (1), (3), and (4) received largely positive evaluation, judging the efficiency of 4 methods by F measure, a mixture of recall and precision ratio. In particular, decisive cue phrase method was effective to search idea and contextual signal method was effective to detect quasi-idea.

keywords
아이디어 마이닝, 단서 어구, 단서 멀티미디어, 문맥 신호, 담화 구절, 발췌 기법, idea mining, decisive cue phrase, cue multimedia, contextual signal, discourse segmentation, extraction method

참고문헌

1.

Al-Halimi, R. K.. (2003). Mining topic signals from text.

2.

Barzilay, R.. (1997). Using lexical chains for text summarization (-). In Proceedings of the Workshop on Intelligent Scalable Text Summarization at the ACL/EACL Conference.

3.

Bergstrom, T.. (2008). Conversation clusters: Human-computer dialog for topic extraction. http://social.cs.uiuc.edu/papers/pdfs/bergstrom-1361.pdf.

4.

Brandow, R.. (1995). Automatic condensation of electronic publications by sentence selection. Information Processing & Management, 31(5), 675-685.

5.

Buitelaar, P.. (2008). Topic extraction from scientific literature for competency management. http://citeseerx.ist.psu.edu/.../download?doi=10.

6.

Businessdictionary. com(n. d. ). http://www.businessdictionary.com/.

7.

정영미. (2008). 사건중심 뉴스기사 자동요약을 위한 사건탐지 기법에 관한 연구. 정보관리학회지, 25(4), 227-243.

8.

Dave, K.. (2004). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. http://www.kushaldave.com/p451-dave.pdf.

9.

Definitions.net (n.d.). http://www.definitions.net/.

10.

Dey. L.. (2008). Opinion mining fom noisy text data. http://dl.acm.org/citation.cfm?id=1390763.

11.

Dong-A Daily News.

12.

Edmundson, H. P.. (1998). New methods in automatic extracting, In Indexing and abstracting in theory and practice:Library Association Publishing.

13.

Hovy, E.. (1999). Automated text summarization in SUMMARIST (-). In Proceedings of the Workshop on Gaps and Bridges in NL Planning and Generation, 53-58. ECAI Conference.

14.

Kupiec, J.. (1995). A trainable document summarizer (68-73). Proceedings of the Eighteenth Annual International ACM Conference on Research.

15.

이지혜. (2009). 지도적 잠재의미색인(LSI)기법을 이용한 의견 문서 자동 분류에 관한 실험적 연구. 정보관리학회지, 26(3), 451-462.

16.

이태영. (2005). 자동 발췌문/요약 시스템 구축에 관한 연구- 학술지 논문기사를 중심으로 -. 한국문헌정보학회지, 39(3), 139-163.

17.

Liu, B.. (2009). Opinion mining. http://www.cs.uic.edu/~liub/FBS/opinion-mining.pdf..

18.

Mani, I.. (2001). Automatic summarization:John Benjamins Publishing Company.

19.

Manning, C. D.. (2008). Introduction to information retrieval:Cambridge University Press.

20.

Marcu, D.. (1999). Discourse trees are good indicators of importance in text, In Advanced in Automatic Text Summarization:TheMIT Press.

21.

Meadow, C. T.. (2000). Text information retrieval systems:Academic Press.

22.

Myaeng, S. H.. (1999). Development and evaluation of a statistically-based document summarization system, In Advanced in Automatic Text Summarization:The MIT Press.

23.

Pang, B.. (2008). Opinion mining and sentiment analysis. Retrieved. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.1344.

24.

Roth, B.. (2007). Topic extraction and relation in instant messaging. http://nlp.stanford.edu/courses/cs224n/2010/reports/rothben.pdf.

25.

Schutze, H.. (1998). Automatic word sense discrimination. Computational Linguistics, 24(1), 97-123.

26.

Teufel, S.. (1999). Argumentive classification of extracted sentences as a first step towards flexible abstracting, In Advanced in Automatic Text Summarization:the MIT Press.

27.

The free dictionary (n.d.). www.thefreedictionary.com/.

28.

Thorleuchter, D.. (2008). Finding new technological ideas and inventions with text mining and technique philosophy. http://www.springerlink.com/content/j21800t0768x6644/.

29.

Thorleuchter, D.. (2009). Mining ideas from textual information. http://www.feb.ugent.be/nl/Ondz/wp/Papers/wp_09_619.pdf.

30.

Wang, X.. (2008). Mining common topics from multiple asynchronous text streams. http://wsdm2009.org/papers/p192-wang.pdf.

31.

Webster online dictionary (n.d.). http://www.websters-online-dictionary.org/.

32.

Wikipedia (n.d.). http://ko.wikipedia.org/wiki/.

33.

Yourdictionary.com (n.d.). www.yourdictionary.com.

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