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Commonsense knowledge enhanced memory network for stance classification
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Du, Jiachen, Gui, Lin, Xu, Ruifeng, Xia, Yunqing and Wang, Xuan (2020) Commonsense knowledge enhanced memory network for stance classification. IEEE Intelligent Systems, 35 (4). pp. 102-109. doi:10.1109/MIS.2020.2983497
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Official URL: https://doi.org/10.1109/MIS.2020.2983497
Abstract
Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science > Computer Science | ||||||||
| Journal or Publication Title: | IEEE Intelligent Systems | ||||||||
| Publisher: | IEEE Computer Society | ||||||||
| ISSN: | 1541-1672 | ||||||||
| Official Date: | July 2020 | ||||||||
| Dates: |
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| Volume: | 35 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 102-109 | ||||||||
| DOI: | 10.1109/MIS.2020.2983497 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
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