Automatic Narrative Humor Recognition Method Using Machine Learning and Semantic Similarity Based Punchline Detection


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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/63638

Title:	Automatic Narrative Humor Recognition Method Using Machine Learning and Semantic Similarity Based Punchline Detection
Authors:	Rzepka, Rafal Browse this author →KAKEN DB
	Amaya, Yusuke Browse this author
	Yatsu, Motoki Browse this author
	Araki, Kenji Browse this author
Issue Date:	26-Jul-2015
Publisher:	IJCAI
Journal Title:	International Workshop on Chance Discovery, Data Synthesis and Data Market in IJCAI2015
Abstract:	In this paper we introduce our method for recognizing jokes written in Japanese language by where the punchline is detected using WordNet. The results showed that when compared to method based on Bayesian posterior probability baseline, the pro- posed system achieved 5.3 point increase in recall and 2.6 point increase in classification accuracy. Our work1 is the first challenge to detect humor in Japanese language and this ability can be utilized not only for more natural reactions while perceiving user’s utterance, but also for discovering funny stories to be uttered by an agent.
Conference Name:	International Workshop on Chance Discovery, Data Synthesis and Data Market in IJCAI2015
Conference Place:	Buenos Aires
Type:	proceedings
URI:	http://hdl.handle.net/2115/63638
Appears in Collections:	情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: RZEPKA Rafal

OAI-PMH ( junii2 , jpcoar_1.0 )

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