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Connecting targets to tweets : semantic attention-based model for target-specific stance detection
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Zhou, Yiwei and Cristea, Alexandra I. (2017) Connecting targets to tweets : semantic attention-based model for target-specific stance detection. In: 18th International Conference on Web Information Systems Engineering (WISE'17), Moscow , 07-11 Oct 2017. Published in: Web Information Systems Engineering – WISE 2017, 10569 ; 10570 pp. 18-32. ISBN 9783319687827. ISSN 0302-9743.
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Official URL: https://doi.org/10.1007/978-3-319-68783-4_2
Abstract
Understanding what people say and really mean in tweets is still a wide open research question. In particular, understanding the stance of a tweet, which is determined not only by its content, but also by the given target, is a very recent research aim of the community. It still remains a challenge to construct a tweet’s vector representation with respect to the target, especially when the target is only implicitly mentioned, or not mentioned at all in the tweet. We believe that better performance can be obtained by incorporating the information of the target into the tweet’s vector representation. In this paper, we thus propose to embed a novel attention mechanism at the semantic level in the bi-directional GRU-CNN structure, which is more fine-grained than the existing token-level attention mechanism. This novel attention mechanism allows the model to automatically attend to useful semantic features of informative tokens in deciding the target-specific stance, which further results in a conditional vector representation of the tweet, with respect to the given target. We evaluate our proposed model on a recent, widely applied benchmark Stance Detection dataset from Twitter for the SemEval-2016 Task 6.A. Experimental results demonstrate that the proposed model substantially outperforms several strong baselines, which include the state-of-the-art token-level attention mechanism on bi-directional GRU outputs and the SVM classifier.
Item Type: | Conference Item (Paper) | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Series Name: | Lecture Notes in Computer Science | ||||||
Journal or Publication Title: | Web Information Systems Engineering – WISE 2017 | ||||||
Publisher: | Springer International Publishing | ||||||
Place of Publication: | Cham | ||||||
ISBN: | 9783319687827 | ||||||
ISSN: | 0302-9743 | ||||||
Book Title: | Web Information Systems Engineering – WISE 2017 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I | ||||||
Editor: | Bouguettaya , A.. | ||||||
Official Date: | 4 October 2017 | ||||||
Dates: |
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Volume: | 10569 ; 10570 | ||||||
Page Range: | pp. 18-32 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Description: | The two-volume set LNCS 10569 and LNCS 10570 constitutes the proceedings of the 18th International Conference on Web Information Systems Engineering, WISE 2017, held in Puschino, Russia, in October 2017. |
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Date of first compliant deposit: | 1 February 2018 | ||||||
Date of first compliant Open Access: | 1 February 2018 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 18th International Conference on Web Information Systems Engineering (WISE'17) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Moscow | ||||||
Date(s) of Event: | 07-11 Oct 2017 | ||||||
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