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Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Homographic Puns Recognition Based on Latent Semantic Structures

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

Published in: Natural Language Processing and Chinese Computing

Publisher: 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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Footnotes
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Metadata
Title
Homographic Puns Recognition Based on Latent Semantic Structures
Authors
Yufeng Diao
Liang Yang
Dongyu Zhang
Linhong Xu
Xiaochao Fan
Di Wu
Hongfei Lin
Copyright Year
2018
Book
Natural Language Processing and Chinese Computing

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

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

Copyright Year: 2018

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

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