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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 許永真(Yung-Jen Hsu) | |
dc.contributor.author | Chi-Chin Lin | en |
dc.contributor.author | 林自均 | zh_TW |
dc.date.accessioned | 2021-05-15T17:50:34Z | - |
dc.date.available | 2014-08-25 | |
dc.date.available | 2021-05-15T17:50:34Z | - |
dc.date.copyright | 2014-08-25 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-08-19 | |
dc.identifier.citation | [1] C. Bauckhage. Insights into internet memes. In L. A. Adamic, R. A. Baeza-Yates, and S. Counts, editors, Proceedings of the Fifth International Conference on Weblogs and Social Media. The AAAI Press, 2011.
[2] C. Bauckhage, K. Kersting, and F. Hadiji. Mathematical models of fads explain the temporal dynamics of internet memes. In Proceedings of the Seventh International Conference on Weblogs and Social Media, 2013. [3] M. S. Bernstein, G. Little, R. C. Miller, B. Hartmann, M. S. Ackerman, D. R. Karger, D. Crowell, and K. Panovich. Soylent: A word processor with a crowd inside. In Proceedings of the 23rd Annual ACM Symposium on User Interface Software and Technology, pages 313–322, New York, NY, USA, 2010. ACM. [4] J. P. Bigham, C. Jayant, H. Ji, G. Little, A. Miller, R. C. Miller, R. Miller, A. Tatarowicz, B. White, S. White, and T. Yeh. Vizwiz: Nearly real-time answers to visual questions. In Proceedings of the 23rd Annual ACM Symposium on User Interface Software and Technology, pages 333–342, New York, NY, USA, 2010. ACM. [5] M. Coscia. Competition and success in the meme pool: a case study on quickmeme.com. International Conference of Weblogs and Social Media, abs/1304.1712, 2013. [6] J. Costa, C. Silva, M. Antunes, and B. Ribeiro. On using crowdsourcing and active learning to improve classification performance. In International Conference on Intelligent Systems Design and Applications, pages 469–474, Nov 2011. [7] D. Gupta, M. Digiovanni, H. Narita, and K. Goldberg. Jester 2.0 (poster abstract): Evaluation of an new linear time collaborative filtering algorithm. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 291–292, New York, NY, USA, 1999. ACM. [8] C. Kiddon and Y. Brun. That’s what she said: Double entendre identification. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 89–94, Portland, OR, USA, Jun 2011. [9] A. Kittur, E. H. Chi, and B. Suh. Crowdsourcing user studies with mechanical turk. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 453–456, New York, NY, USA, 2008. ACM. [10] R. Mihalcea, C. Strapparava, and S. Pulman. Computational models for incongruity detection in humour. In A. Gelbukh, editor, Computational Linguistics and Intelli- gent Text Processing, volume 6008, pages 364–374. Springer Berlin Heidelberg, 2010. [11] G. Parent and M. Eskenazi. Clustering dictionary definitions using amazon mechanical turk. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, pages 21–29, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics. [12] V. Raskin. Semantic Mechanisms of Humor. D. Reidel, 1 edition, Dec 1985. [13] R. Vaish, K. Wyngarden, J. Chen, B. Cheung, and M. Bernstein. Twitch crowdsourcing: Crowd contributions in short bursts of time. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2014. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/4959 | - |
dc.description.abstract | 在許多的社群網站裡,幽默的圖片是很常見的。 但是,剛到這些網站的新手們通常都很難融入,因為這種次文化通常都有著一些隱涵的資訊。 在幽默圖片的種類裡,網路模因對於新手們是相對比較難理解的。 在這一篇論文裡,我們開發了一套系統可以藉由群眾外包的技術,來產生網路模因的解釋。 我們宣稱只要看過我們產生的解釋之後,就算對於網路模因不熟悉的人, 也都可以很快地了解其中的笑點。我們模板式的解釋可以突顯出笑話裡正常情況和笑點句子的不和諧。 這樣的解釋可以透過兩套人腦計算的流程來產生。 這裡的實驗結果顯示系統所產生的解釋可以很好的幫助新手來了解不熟悉的網路模因。 未來研究的方向可以考慮加入更多電腦計算的幫助,來改善計算人性這個領域的發展。 | zh_TW |
dc.description.abstract | Humorous images can be seen in many social media websites. However, newcomers to these websites often have trouble fitting in because the community subculture is usually implicit. Among all the types of humorous images, Internet memes are relatively hard for newcomers to understand. In this work, we develop a system that leverages crowdsourcing techniques to generate explanations for memes. We claim that people who are not familiar with Internet meme subculture can still quickly pick up the gist of the memes by reading the explanations. Our template-based explanations illustrate the incongruity between normal situations and the punchlines in jokes. The explanations can be produced by completing the two proposed human task processes. Experimental results suggest that the explanations produced by our system greatly help newcomers to understand unfamiliar memes. For further research, it is possible to employ our explanation generation system to improve computational humanities. | en |
dc.description.provenance | Made available in DSpace on 2021-05-15T17:50:34Z (GMT). No. of bitstreams: 1 ntu-103-R01944018-1.pdf: 1286879 bytes, checksum: 99edc5bdb367e0928bd85a5e6e548edb (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | Acknowledgments
i Abstract iii List of Figures vii List of Tables x Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Computational Humor Recognition . . . . . . . . . . . . . . . . . . . 5 2.2 Crowdsourcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Linguistic Humor Theories . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 3 Methodology 9 3.1 Template-Based Explanation . . . . . . . . . . . . . . . . . . . . . . . 3.2 Three Anti-Punchline Generation Methods . . . . . . . . . . . . . . . 11 3.2.1 Collection-Selection Process . . . . . . . . . . . . . . . . . . . 12 3.2.2 Direct Filling Process . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.3 Expert Process . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Supporting Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Chapter 4 Experiments 19 4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 Experiment Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Chapter 5 Discussion 5.1 28 Online Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Chapter 6 Conclusion and Future Work 32 Bibliography 34 | |
dc.language.iso | en | |
dc.title | 群眾外包於幽默網路圖片解釋生成技術之研究 | zh_TW |
dc.title | Crowd-sourced Explanations for Humorous Internet Memes | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林守德(Shou-De Lin),陳昇瑋(Sheng-Wei Chen),蔡宗翰(Tzong-Han Tsai),林光龍(Kuang-Lung Lin) | |
dc.subject.keyword | 電腦幽默辨識,群眾外包,幽默網路圖片, | zh_TW |
dc.subject.keyword | computational humor recognition,crowdsourcing,Internet memes, | en |
dc.relation.page | 35 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2014-08-19 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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