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Two-Target Stance Detection with Target-Related Zone Modeling

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Information Retrieval (CCIR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11168))

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Abstract

Recently, stance detection on Twitter has been a hot research topic in the natural language processing community. Most previous studies have assumed that only one target is involved in a tweet and perform single-target stance detection. In this study, we address a more challenging version of this task, namely two-target stance detection, where two targets are involved. Specifically, we first define four categories of text zones related to two targets, i.e., Target 1, Target 2, Non-target, and Other, and propose an unsupervised approach to automatically obtain these zones. Then, we propose a hierarchical neural network to perform stance detection for each target where multiple LSTM layers are leveraged to encode the target-related zones and a Bidirectional LSTM layer to encode the outputs from the LSTM layers. Moreover, we introduce a target-related attention mechanism in the hierarchical network. Empirical studies demonstrate the effectiveness of the proposed approach to two-target stance detection.

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Acknowledgments

This research work has been partially supported by two NSFC grants, No. 61672366 and No. 61331011.

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Correspondence to Shoushan Li .

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Liu, H., Li, S., Zhou, G. (2018). Two-Target Stance Detection with Target-Related Zone Modeling. In: Zhang, S., Liu, TY., Li, X., Guo, J., Li, C. (eds) Information Retrieval. CCIR 2018. Lecture Notes in Computer Science(), vol 11168. Springer, Cham. https://doi.org/10.1007/978-3-030-01012-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-01012-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01011-9

  • Online ISBN: 978-3-030-01012-6

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