Learning Stance Classification with Recurrent Neural Capsule Network

Lianjie Sun; Xutao Li; Bowen Zhang; Yunming Ye; Baoxun Xu

doi:10.1007/978-3-030-32233-5_22

Learning Stance Classification with Recurrent Neural Capsule Network

Lianjie Sun
, Xutao Li^*
, Bowen Zhang
, Yunming Ye
, Baoxun Xu

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology
Shenzhen Stock Exchange

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Stance classification is a natural language processing (NLP) task to detect author’s stance when give a specific target and context, which can be applied in online debating forum, e.g., Twitter, Weibo, etc. In this paper, we present a novel target orientation recurrent neural capsule network, called TRNN-Capsule to solve the problem. In TRNN-Capsule, the target and context are both encoded by leveraging a bidirectional LSTM model. Then, capsule blocks are appended to produce the final classification outcome. Experiments on two benchmark data sets are conducted and the results show that the proposed TRNN-Capsule outperforms state-of-the-art competitors for the stance classification task.

Original language	English
Title of host publication	Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings
Editors	Jie Tang, Min-Yen Kan, Dongyan Zhao, Sujian Li, Hongying Zan
Publisher	Springer
Pages	277-289
Number of pages	13
ISBN (Print)	9783030322328
DOIs	https://doi.org/10.1007/978-3-030-32233-5_22
State	Published - 2019
Externally published	Yes
Event	8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019 - Dunhuang, China Duration: 9 Oct 2019 → 14 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11838 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019
Country/Territory	China
City	Dunhuang
Period	9/10/19 → 14/10/19

Keywords

RNN Capsule Network
Stance classification

Access to Document

10.1007/978-3-030-32233-5_22

Cite this

Sun, L., Li, X., Zhang, B., Ye, Y., & Xu, B. (2019). Learning Stance Classification with Recurrent Neural Capsule Network. In J. Tang, M.-Y. Kan, D. Zhao, S. Li, & H. Zan (Eds.), Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings (pp. 277-289). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11838 LNAI). Springer. https://doi.org/10.1007/978-3-030-32233-5_22

Sun, Lianjie ; Li, Xutao ; Zhang, Bowen et al. / Learning Stance Classification with Recurrent Neural Capsule Network. Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings. editor / Jie Tang ; Min-Yen Kan ; Dongyan Zhao ; Sujian Li ; Hongying Zan. Springer, 2019. pp. 277-289 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{496daf94bcda4aceadb5a4c219d5fab5,

title = "Learning Stance Classification with Recurrent Neural Capsule Network",

abstract = "Stance classification is a natural language processing (NLP) task to detect author{\textquoteright}s stance when give a specific target and context, which can be applied in online debating forum, e.g., Twitter, Weibo, etc. In this paper, we present a novel target orientation recurrent neural capsule network, called TRNN-Capsule to solve the problem. In TRNN-Capsule, the target and context are both encoded by leveraging a bidirectional LSTM model. Then, capsule blocks are appended to produce the final classification outcome. Experiments on two benchmark data sets are conducted and the results show that the proposed TRNN-Capsule outperforms state-of-the-art competitors for the stance classification task.",

keywords = "RNN Capsule Network, Stance classification",

author = "Lianjie Sun and Xutao Li and Bowen Zhang and Yunming Ye and Baoxun Xu",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019 ; Conference date: 09-10-2019 Through 14-10-2019",

year = "2019",

doi = "10.1007/978-3-030-32233-5\_22",

language = "英语",

isbn = "9783030322328",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "277--289",

editor = "Jie Tang and Min-Yen Kan and Dongyan Zhao and Sujian Li and Hongying Zan",

booktitle = "Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings",

address = "德国",

}

Sun, L, Li, X, Zhang, B, Ye, Y & Xu, B 2019, Learning Stance Classification with Recurrent Neural Capsule Network. in J Tang, M-Y Kan, D Zhao, S Li & H Zan (eds), Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11838 LNAI, Springer, pp. 277-289, 8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019, Dunhuang, China, 9/10/19. https://doi.org/10.1007/978-3-030-32233-5_22

Learning Stance Classification with Recurrent Neural Capsule Network. / Sun, Lianjie; Li, Xutao; Zhang, Bowen et al.
Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings. ed. / Jie Tang; Min-Yen Kan; Dongyan Zhao; Sujian Li; Hongying Zan. Springer, 2019. p. 277-289 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11838 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning Stance Classification with Recurrent Neural Capsule Network

AU - Sun, Lianjie

AU - Li, Xutao

AU - Zhang, Bowen

AU - Ye, Yunming

AU - Xu, Baoxun

PY - 2019

Y1 - 2019

N2 - Stance classification is a natural language processing (NLP) task to detect author’s stance when give a specific target and context, which can be applied in online debating forum, e.g., Twitter, Weibo, etc. In this paper, we present a novel target orientation recurrent neural capsule network, called TRNN-Capsule to solve the problem. In TRNN-Capsule, the target and context are both encoded by leveraging a bidirectional LSTM model. Then, capsule blocks are appended to produce the final classification outcome. Experiments on two benchmark data sets are conducted and the results show that the proposed TRNN-Capsule outperforms state-of-the-art competitors for the stance classification task.

AB - Stance classification is a natural language processing (NLP) task to detect author’s stance when give a specific target and context, which can be applied in online debating forum, e.g., Twitter, Weibo, etc. In this paper, we present a novel target orientation recurrent neural capsule network, called TRNN-Capsule to solve the problem. In TRNN-Capsule, the target and context are both encoded by leveraging a bidirectional LSTM model. Then, capsule blocks are appended to produce the final classification outcome. Experiments on two benchmark data sets are conducted and the results show that the proposed TRNN-Capsule outperforms state-of-the-art competitors for the stance classification task.

KW - RNN Capsule Network

KW - Stance classification

UR - https://www.scopus.com/pages/publications/85075572539

U2 - 10.1007/978-3-030-32233-5_22

DO - 10.1007/978-3-030-32233-5_22

M3 - 会议稿件

AN - SCOPUS:85075572539

SN - 9783030322328

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 277

EP - 289

BT - Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings

A2 - Tang, Jie

A2 - Kan, Min-Yen

A2 - Zhao, Dongyan

A2 - Li, Sujian

A2 - Zan, Hongying

PB - Springer

T2 - 8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

Y2 - 9 October 2019 through 14 October 2019

ER -

Sun L, Li X, Zhang B, Ye Y, Xu B. Learning Stance Classification with Recurrent Neural Capsule Network. In Tang J, Kan MY, Zhao D, Li S, Zan H, editors, Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings. Springer. 2019. p. 277-289. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32233-5_22

Learning Stance Classification with Recurrent Neural Capsule Network

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Keywords

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