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Target-adaptive graph for cross-target stance detection
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Liang, Bin, Fu, Yonghao, Gui, Lin, Yang, Min, Du, Jiachen, He, Yulan and Xu, Ruifeng (2021) Target-adaptive graph for cross-target stance detection. In: The Web conference 2021, Virtual conference, 12-23 Apr 2021. Published in: Proceedings of Web Conference 2021 pp. 3453-3464. ISBN 9781450383127. doi:10.1145/3442381.3449790
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Official URL: https://doi.org/10.1145/3442381.3449790
Abstract
Target plays an essential role in stance detection of an opinionated review/claim, since the stance expressed in the text often depends on the target. In practice, we need to deal with targets unseen in the annotated training data. As such, detecting stance for an unknown or unseen target is an important research problem. This paper presents a novel approach that automatically identifies and adapts the target-dependent and target-independent roles that a word plays with respect to a specific target in stance expressions, so as to achieve cross-target stance detection. More concretely, we explore a novel solution of constructing heterogeneous target-adaptive pragmatics dependency graphs (TPDG) for each sentence towards a given target. An in-target graph is constructed to produce inherent pragmatics dependencies of words for a distinct target. In addition, another cross-target graph is constructed to develop the versatility of words across all targets for boosting the learning of dominant word-level stance expressions available to an unknown target. A novel graph-aware model with interactive Graphical Convolutional Network (GCN) blocks is developed to derive the target-adaptive graph representation of the context for stance detection. The experimental results on a number of benchmark datasets show that our proposed model outperforms state-of-the-art methods in cross-target stance detection.
Item Type: | Conference Item (Paper) | ||||||||||||||||||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||||||||||||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Sentiment analysis , Computational linguistics -- Network analysis , Data mining, Neural networks (Computer science), Graphy theory | ||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | Proceedings of Web Conference 2021 | ||||||||||||||||||||||||||||||||||||
Publisher: | ACM | ||||||||||||||||||||||||||||||||||||
ISBN: | 9781450383127 | ||||||||||||||||||||||||||||||||||||
Official Date: | 3 June 2021 | ||||||||||||||||||||||||||||||||||||
Dates: |
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Page Range: | pp. 3453-3464 | ||||||||||||||||||||||||||||||||||||
DOI: | 10.1145/3442381.3449790 | ||||||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 4 March 2021 | ||||||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 5 March 2021 | ||||||||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||||||||||||||||||||||||||||||||
Title of Event: | The Web conference 2021 | ||||||||||||||||||||||||||||||||||||
Type of Event: | Conference | ||||||||||||||||||||||||||||||||||||
Location of Event: | Virtual conference | ||||||||||||||||||||||||||||||||||||
Date(s) of Event: | 12-23 Apr 2021 | ||||||||||||||||||||||||||||||||||||
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