Exploiting Syntactic Structures for Humor Recognition

Humor recognition is an interesting and challenging task in natural language processing. This paper proposes to exploit syntactic structure features to enhance humor recognition. Our method achieves significant improvements compared with humor theory driven baselines. We found that some syntactic structure features consistently correlate with humor, which indicate interesting linguistic phenomena. Both the experimental results and the analysis demonstrate that humor can be viewed as a kind of style and content independent syntactic structures can help identify humor and have good interpretability.

[1]  Carlo Strapparava,et al.  Making Computers Laugh: Investigations in Automatic Humor Recognition , 2005, HLT.

[2]  Iryna Gurevych,et al.  Automatic disambiguation of English puns , 2015, ACL.

[3]  Yuchen Zhang,et al.  CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes , 2012, EMNLP-CoNLL Shared Task.

[4]  Ani Nenkova,et al.  Structural Features for Predicting the Linguistic Quality of Text - Applications to Machine Translation, Automatic Summarization and Human-Authored Text , 2009, Empirical Methods in Natural Language Generation.

[5]  Rod A. Martin,et al.  Humor and Life Stress , 1986 .

[6]  Lizhen Liu,et al.  Modeling Sentiment Association in Discourse for Humor Recognition , 2018, ACL.

[7]  Diyi Yang,et al.  Humor Recognition and Humor Anchor Extraction , 2015, EMNLP.

[8]  Jason Rutter,et al.  Stand-up as interaction : performance and audience in comedy venues , 1997 .

[9]  Julia Taylor Rayz,et al.  Computationally Recognizing Wordplay in Jokes , 2004 .

[10]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[11]  Charles R. Gruner,et al.  The Game of Humor: A Comprehensive Theory of Why We Laugh , 1997 .

[12]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[13]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[14]  Diane J. Litman,et al.  Humor: Prosody Analysis and Automatic Recognition for F*R*I*E*N*D*S* , 2006, EMNLP.

[15]  J. Suls.  Chapter 4 – A Two-Stage Model for the Appreciation of Jokes and Cartoons: An Information-Processing Analysis , 1972 .

[16]  P. Keith-Spiegel.  Chapter 1 – Early Conceptions of Humor: Varieties and Issues , 1972 .

[17]  Renxian Zhang,et al.  Recognizing Humor on Twitter , 2014, CIKM.

[18]  W. Martineau,et al.  Chapter 5 – A Model of the Social Functions of Humor , 1972 .

[19]  Matthew H. Davis,et al.  Why Clowns Taste Funny: The Relationship between Humor and Semantic Ambiguity , 2011, The Journal of Neuroscience.

[20]  James Allan,et al.  Joke retrieval: recognizing the same joke told differently , 2008, CIKM '08.

[21]  Craig A. Anderson,et al.  An Examination of Perceived Control, Humor, Irrational Beliefs, and Positive Stress as Moderators of the Relation Between Negative Stress and Health , 1989 .

[22]  Dan Klein,et al.  Accurate Unlexicalized Parsing , 2003, ACL.

[23]  Victor Raskin,et al.  Semantic mechanisms of humor , 1984 .

[24]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[25]  Rada Mihalcea,et al.  Characterizing Humour: An Exploration of Features in Humorous Texts , 2009, CICLing.

[26]  Mitchell P. Marcus,et al.  OntoNotes: The 90% Solution , 2006, NAACL.