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Titlebook: Investigations in Computational Sarcasm; Aditya Joshi,Pushpak Bhattacharyya,Mark J. Carman Book 2018 Springer Nature Singapore Pte Ltd. 20

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发表于 2025-3-21 17:42:02 | 显示全部楼层 |阅读模式
书目名称Investigations in Computational Sarcasm
编辑Aditya Joshi,Pushpak Bhattacharyya,Mark J. Carman
视频videohttp://file.papertrans.cn/475/474798/474798.mp4
概述Provides a tabular summary of the past work on computational sarcasm.Lays down the linguistic foundations for computational sarcasm.Presents elaborate examples motivating each work module.Describes ap
丛书名称Cognitive Systems Monographs
图书封面Titlebook: Investigations in Computational Sarcasm;  Aditya Joshi,Pushpak Bhattacharyya,Mark J. Carman Book 2018 Springer Nature Singapore Pte Ltd. 20
描述..This book describes the authors’ investigations of computational sarcasm based on the notion of incongruity. In addition, it provides a holistic view of past work in computational sarcasm and the challenges and opportunities that lie ahead. Sarcastic text is a peculiar form of sentiment expression and computational sarcasm refers to computational techniques that process sarcastic text. To first understand the phenomenon of sarcasm, three studies are conducted: (a) how is sarcasm annotation impacted when done by non-native annotators? (b) How is sarcasm annotation impacted when the task is to distinguish between sarcasm and irony? And (c) can targets of sarcasm be identified by humans and computers. Following these studies, the book proposes approaches for two research problems: sarcasm detection and sarcasm generation. To detect sarcasm, incongruity is captured in two ways: ‘intra-textual incongruity’ where the authors look at incongruity within the text to be classified (i.e., target text) and ‘context incongruity’ where the authors incorporate information outside the target text. These approaches use machine-learning techniques such as classifiers, topic models, sequence labell
出版日期Book 2018
关键词Sentiment analysis; Sarcasm detection; sarcasm generation; Irony markers; Opinion mining; Computational i
版次1
doihttps://doi.org/10.1007/978-981-10-8396-9
isbn_softcover978-981-13-4139-7
isbn_ebook978-981-10-8396-9Series ISSN 1867-4925 Series E-ISSN 1867-4933
issn_series 1867-4925
copyrightSpringer Nature Singapore Pte Ltd. 2018
The information of publication is updating

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发表于 2025-3-21 22:08:20 | 显示全部楼层
Aditya Joshi,Pushpak Bhattacharyya,Mark J. Carmanborescences and making extensive use of the subproblem is proposed. A local optimum is defined and the solution procedure is shown to converge to this local optimum in a finite number of iterations. Numerical experience with the algorithm is included.
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Aditya Joshi,Pushpak Bhattacharyya,Mark J. Carmanesign centering, optimal assignment of manufacturing tolerances and postproduction tuning. The inclusion of model and environmental uncertainties is discussed. Practical examples illustrate the current state of the art. Difficulties facing the design optimizer as well as directions of possible futur
发表于 2025-3-22 08:33:34 | 显示全部楼层
Aditya Joshi,Pushpak Bhattacharyya,Mark J. Carman editors hope that this book will also contribute towards new ideas and concepts in a world of ever decreasing natural resources and ever increasing demands for lighter and yet stronger and safer technical components. I"inally, the editors wish to thank all colleagues who helped in the organisation
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e spent his engineering career designing better ways to kill people or to destroy property – the sole purpose of a bomb. I wondered how many people had been killed because this man had dev- oped a clever acoust978-1-4471-5822-6978-1-84882-674-8
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Book 2018ual incongruity’ where the authors look at incongruity within the text to be classified (i.e., target text) and ‘context incongruity’ where the authors incorporate information outside the target text. These approaches use machine-learning techniques such as classifiers, topic models, sequence labell
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