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Erschienen in:
Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Homographic Puns Recognition Based on Latent Semantic Structures

verfasst von : Yufeng Diao, Liang Yang, Dongyu Zhang, Linhong Xu, Xiaochao Fan, Di Wu, Hongfei Lin

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Homographic puns have a long history in human writing, being a common source of humor in jokes and other comedic works. It remains a difficult challenge to construct computational models to discover the latent semantic structures behind homographic puns so as to recognize puns. In this work, we design several latent semantic structures of homographic puns based on relevant theory and design sets of effective features of each structure, and then we apply an effective computational approach to identify homographic puns. Results on the SemEval2017 Task7 and Pun of the Day datasets indicate that our proposed latent semantic structures and features have sufficient effectiveness to distinguish between homographic pun and non-homographic pun texts. We believe that our novel findings will facilitate and stimulate the booming field of computational pun research in the future.

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Vorheriges Kapitel Biomedical Domain-Oriented Word Embeddings via Small Background Texts for Biomedical Text Mining Tasks
Nächstes Kapitel Constructing a Chinese Conversation Corpus for Sentiment Analysis
Fußnoten
Literatur
1.
Pollack, J.: The Pun Also Rises. Penguin Publishing Group (2011)
2.
Tanaka, K.: The pun in advertising: a pragmatic approach. Lingua 87(1), 91–102 (1992)CrossRef
3.
Redfern, W.: Puns. Scriblerian Kit-Cats 19(2), 204 (1987)
4.
Delabastita, D.: Focus on the pun: wordlplay as a special problem in translation studies. Target 6(2), 223–243 (1994)CrossRef
5.
Miller, T., Turković, M.: Towards the automatic detection and identification of English puns. Eur. J. Humour Res. 4(1), 59–75 (2016)CrossRef
6.
Kao, J.T., Levy, R., Goodman, N.D.: A computational model of linguistic humor in puns. Cogn. Sci. 40(5), 1270–1285 (2015)CrossRef
7.
Jaech, A., Koncel-Kedziorski, R., Ostendorf, M.: Phonological pun-derstanding. In: Proceedings of NAACL-HLT, pp. 654–663 (2016)
8.
Miller, T., Gurevych, I.: Automatic disambiguation of English puns. In: ACL, vol. 1, pp. 719–729 (2015)
9.
Huang, Y.H., Huang, H.H., Chen, H.H.: Identification of homographic pun location for pun understanding. In: Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, pp. 797–798 (2017)
10.
Hempelmann, C.F.: Paronomasic puns: target recoverability towards automatic generation. Diss. Abs. Int. 64(11), 4029 (2003)
11.
Hong, B.A., Ong, E.: Automatically extracting word relationships as templates for pun generation. In: The Workshop on Computational Approaches to Linguistic Creativity. Association for Computational Linguistics, pp. 24–31 (2010)
12.
Taylor, J.M., Mazlack, L.J.: Computationally recognizing wordplay in jokes. In: Proceedings of the 26th Annual Conference of the Cognitive Science Society (CogSci 2004), pp. 1315–1320 (2004)
13.
Taylor, J.M.: Computational detection of humor: a dream or a nightmare? The ontological semantics approach. In: Proceedings of the 2009 ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 429–432 (2009)
14.
Yang, D., Lavie, A., Dyer, C., et al.: Humor recognition and humor anchor extraction. In: EMNLP, pp. 2367–2376 (2015)
15.
Valitutti, A., Strapparava, C., Stock, O.: Textual affect sensing for computational advertising. In: Proceedings of the AAAI Spring Symposium on Creative Intelligent Systems, pp. 117–122, March 2008
16.
Monnot, M.: Puns in advertising: ambiguity as verbal aggression. Maledicta 6, 7–20 (1982)
17.
Hempelmann, C.F.: Computational humor: beyond the pun? In: Raskin, V. (ed.) The Primer of Humor Research. Humor Research, vol. 8, pp. 333–360. Mouton de Gruyter, Berlin (2008)
18.
Morkes, J., Kernal, H.K., Nass, C.: Effects of humor in task-oriented human–computer interaction and computer-mediated communication: a direct test of SRCT theory. Hum.-Comput. Interact. 14(4), 395–435 (1999)CrossRef
19.
Mormot, M., Adelstein, A., Bulusu, L.: Immigrant mortality in England and Wales 1970–1978. Popul. Trends 20
20.
Lems, K.: Laughing all the way: teaching English using puns. In: English Teaching Forum. US Department of State. Bureau of Educational and Cultural Affairs, Office of English Language Programs, SA-5, 2200 C Street NW 4th Floor, Washington, DC 20037, vol. 51, no. 1, pp. 26–33 (2013)
21.
Wales, K.: A Dictionary of Stylistics. Routledge, Abingdon (2014)
22.
Heafield, K., Pouzyrevsky, I., Clark, J.H., Koehn, P.: Scalable modified Kneser-Ney language model estimation. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, pp. 690–696 (2013)
23.
Kucera, H., Francis, W.N.: Computational Analysis of Present-Day American English. Brown University Press, Providence (1967)
24.
Bekinschtein, T.A., Davis, M.H., Rodd, J.M., et al.: Why clowns taste funny: the relationship between humor and semantic ambiguity. J. Neurosci. 31(26), 9665–9671 (2011)CrossRef
25.
Reyes, A., Rosso, P., Buscaldi, D.: From humor recognition to irony detection: the figurative language of social media. Data Knowl. Eng. 74, 1–12 (2012)CrossRef
26.
Van Mulken, M., Van Enschot-van, D.R., Hoeken, H.: Puns, relevance and appreciation in advertisements. J. Pragmat. 37(5), 707–721 (2005)CrossRef
27.
28.
Barbieri, F., Saggion, H.: Modelling irony in Twitter: feature analysis and evaluation. In: LREC, pp. 4258–4264 (2014)
29.
Mikolov, T., Sutskever, I., Chen, K., et al.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
30.
Zhang, R., Liu, N.: Recognizing humor on Twitter. In: ACM International Conference on Conference on Information and Knowledge Management, pp. 889–898. ACM (2014)
31.
Miller, T., Hempelmann, C.F., Gurevych, I.: SemEval-2017 task 7: detection and interpretation of English puns. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Vancouver, BC (2017)
Metadaten
Titel
Homographic Puns Recognition Based on Latent Semantic Structures
verfasst von
Yufeng Diao
Liang Yang
Dongyu Zhang
Linhong Xu
Xiaochao Fan
Di Wu
Hongfei Lin
Copyright-Jahr
2018
Buch
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-319-73617-4

Electronic ISBN: 978-3-319-73618-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-73618-1_47