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Affective dependency graph for sarcasm detection
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Lou, Chenwei, Bin, Liang, Gui, Lin, He, Yulan, Dang, Yixue and Xu, Ruifeng (2021) Affective dependency graph for sarcasm detection. In: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Online, 11-15 Jul 2021. Published in: SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval pp. 1844-1849. ISBN 9781450380379. doi:10.1145/3404835.3463061
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WRAP-affective-dependency-graph-sarcasm-detection-Gui-2021.pdf - Accepted Version - Requires a PDF viewer. Download (2064Kb) | Preview |
Official URL: https://doi.org/10.1145/3404835.3463061
Abstract
Detecting sarcastic expressions could promote the understanding of natural language in social media. In this paper, we revisit sarcasm detection from a novel perspective, so as to account for the longrange literal sentiment inconsistencies. More concretely, we explore a novel scenario of constructing an affective graph and a dependency graph for each sentence based on the affective information retrieved from external affective commonsense knowledge and the syntactical information of the sentence. Based on it, an Affective Dependency Graph Convolutional Network (ADGCN) framework is proposed to draw long-range incongruity patterns and inconsistent expressions over the context for sarcasm detection by means with interactively modeling the affective and dependency information. Experimental results on multiple benchmark datasets show that our proposed approach outperforms the current state-of-the-art methods in sarcasm detection.
| Item Type: | Conference Item (Paper) | |||||||||||||||||||||
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| Subjects: | B Philosophy. Psychology. Religion > BH Aesthetics Q Science > QA Mathematics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Irony, Natural language processing (Computer science) , Sentiment analysis | |||||||||||||||||||||
| Journal or Publication Title: | SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval | |||||||||||||||||||||
| Publisher: | ACM | |||||||||||||||||||||
| ISBN: | 9781450380379 | |||||||||||||||||||||
| Official Date: | 11 July 2021 | |||||||||||||||||||||
| Dates: |
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| Page Range: | pp. 1844-1849 | |||||||||||||||||||||
| DOI: | 10.1145/3404835.3463061 | |||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||
| Reuse Statement (publisher, data, author rights): | © ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), (2021) https://doi.org/10.1145/3404835.3463061 | |||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
| Date of first compliant deposit: | 3 June 2021 | |||||||||||||||||||||
| Date of first compliant Open Access: | 3 June 2021 | |||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||||||||||||||
| Title of Event: | The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) | |||||||||||||||||||||
| Type of Event: | Conference | |||||||||||||||||||||
| Location of Event: | Online | |||||||||||||||||||||
| Date(s) of Event: | 11-15 Jul 2021 | |||||||||||||||||||||
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