-검정.jpg){kind=logo}
- P-ISSN1225-598X
- E-ISSN2982-6292
스피치 요약을 위한 태그의미분석과 잠재의미분석간의 비교 연구
Comparing the Use of Semantic Relations between Tags Versus Latent Semantic Analysis for Speech Summarization
초록
본 연구는 스피치 요약을 위해서 태그를 확장하고 또한 태그 간의 의미적 관계 정보를 이용할 수 있는 태그의미분석 방법을 제안하고 평가하였다. 이를 위해서, 먼저 비디오 태그를 확장하고 태그 간의 의미적 관계를 분석하는데 있어서 플리커의 태그 클러스터와 워드넷의 동의어 정보가 얼마나 효과적으로 이용될 수 있는가 조사해 보았다. 그런 다음 태그의미분석 방법의 특성과 효율성을 조사해 보기 위해서 제안한 방법을 잠재의미분석(Latent Semantic Analysis) 방법과 비교해 보았다. 분석 결과, 플리커의 태그 클러스터는 효과적으로 이용되었지만 워드넷은 효과적으로 이용되지 못한 것으로 나타났다. F측정을 사용하여 두 방법의 효율성을 비교한 결과, 제안한 방법의 F값(0.27)이 잠재의미분석 방법의 F값(0.22)보다 높게 나타났다.
- keywords
- Expanded Tags, Latent Semantic Analysis, TED Talks, Flickr Tag Clusters, WordNet, 일반 스피치 요약, 비디오, 태그의미분석, 확장된 태그, 태그 클러스터, 잠재의미분석, F측정, 강의 자료, 플리커, 유투브, 내재적 평가, Expanded Tags, Latent Semantic Analysis, TED Talks, Flickr Tag Clusters, WordNet
Abstract
We proposed and evaluated a tag semantic analysis method in which original tags are expanded and the semantic relations between original or expanded tags are used to extract key sentences from lecture speech transcripts. To do that, we first investigated how useful Flickr tag clusters and WordNet synonyms are for expanding tags and for detecting the semantic relations between tags. Then, to evaluate our proposed method, we compared it with a latent semantic analysis (LSA) method. As a result, we found that Flick tag clusters are more effective than WordNet synonyms and that the F measure mean (0.27) of the tag semantic analysis method is higher than that of LSA method (0.22).
- keywords
- Expanded Tags, Latent Semantic Analysis, TED Talks, Flickr Tag Clusters, WordNet, 일반 스피치 요약, 비디오, 태그의미분석, 확장된 태그, 태그 클러스터, 잠재의미분석, F측정, 강의 자료, 플리커, 유투브, 내재적 평가, Expanded Tags, Latent Semantic Analysis, TED Talks, Flickr Tag Clusters, WordNet
참고문헌
김현희. 2009. 비디오의 오디오 정보 요약 기법에 관한 연구. ..정보관리학회지.., 26(3): 169-188.
김현희. 2012. 이용자 태그를 활용한 비디오 스피치 요약의 자동 생성 연구. ..한국문헌정보학회지.., 46(1): 163-181.
정영미. 2005. ..정보검색연구... 서울: 구미무역출판부.
Boydell, O., & Smyth, B. 2010. “Social summarization in collaborative web search.” Information Processing and Management, 46(6): 782-798.
Christensen, H. et al. 2003. “Are extractive text summarisation techniques portable to broadcast news?” Proceedings of Automatic Speech Recognition and Understanding Workshop, St. Thomas,USA, 489-494.
Gong, Y., & Liu, X. 2001. “Generic text summarization using relevance measure and latent semantic analysis.” Proceedings of the 24st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 19-25.
Heckner, M., Neubauer, T., & Wolff, C. 2008. “Tree, funny, to_read, Google: What are tags supposed to achieve?” Proceedings of the 2008 ACM Workshop on Search in Social Media, Napa Valley, California, USA.
Hennig, L. 2009. “Topic-based multi-document summarization with probabilistic latent semantic analysis.” Proceedings of International conference on Recent Advances in NLP, 144-149.
Heu, J. et al. 2013. Multi-document summarization exploiting semantic analysis based on tag cluster. In S. Li et al. (Eds.), Advances in Multimedia Modeling, Lecture Notes in Computer Science, 7733, 479-489.
Hong, R. et al. 2011. “Beyond search: Event driven summarization forweb videos.” ACM Transactions on Multimedia Computing, Communications, and Applications, 7S(1): 24-43.
Kim, H. 2011. “Toward video semantic search based on a structured folksonomy.” Journal of the American Society for Information Science, 62(3): 478-492.
Matusiak, K. 2006. “Towards user-centered indexing in digital image collections.” OCLC Systems & Services: International digital library, 22(4): 283-298.
Ozsoy, M., Alpaslan, F., & Cicekli, I. 2011. “Text summarization using Latent Semantic Analysis.” Journal of Information Science, 37(4): 405-417.
Specia, L., & Motta, E. 2007. Integrating folksonomies with the semantic Web. In E. Franconi, M. Kifer, & W. May (Eds.), The Semantic Web: Research and Applications, Lecture Notes in Computer Science, 4519, 624-639.
Steinberger, J., & Jezek, K. 2004. “Using latent semantic analysis in text summarization and summary evaluation.” Proceedings of ISIM ’04, 93-100.
Yamamoto, D., Masuda, T., Ohira, S., & Nagao, K. 2008. “Collaborative video scene annotation based on tag cloud.” Proceedings of the Advances in Multimedia Information Processing, Tainan, Taiwan, 397-406. <http://tk-www.elcom.nitech.ac.jp/~daisuke/pdf/conf/pcm2008.pdf>.
Zhu, J. et al. 2009. “Tag-oriented document summarization.” Proceedings of the 18th international conference on World Wide Web, 1195-1196.
Wang, M. et al. 2012. “Event driven Web video summarization by tag localization and key-shot identification.” IEEE Transactions on Multimedia, 14(4), 975-985. <http://137.132.145.151/lms/sites/default/files/publication-attachments/wang-06135507.pdf>.
- 다운로드 수
- 조회수
- 0KCI 피인용수
- 0WOS 피인용수