Stance classification with target-specific neural attention networks

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Stance classification, which aims at detecting the stance expressed in text towards a specific target, is an emerging problem in sentiment analysis. A major difference between stance classification and traditional aspect-level sentiment classification is that the identification of stance is dependent on target which might not be explicitly mentioned in text. This indicates that apart from text content, the target information is important to stance detection. To this end, we propose a neural network-based model, which incorporates target-specific information into stance classification by following a novel attention mechanism. In specific, the attention mechanism is expected to locate the critical parts of text which are related to target. Our evaluations on both the English and Chinese Stance Detection datasets show that the proposed model achieves the state-of-the-art performance.

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  • Stance Classification with Target-Specific Neural Attention Networks

    Rights statement: All rights reserved to IJCAI

    Accepted author manuscript, 617 KB, PDF-document

    Embargo ends: 1/01/50


Publication date25 Aug 2017
Publication title26th International Joint Conference on Artificial Intelligence, IJCAI 2017
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages7
ISBN (Electronic)9780999241103
Original languageEnglish
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia


Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017

Bibliographic note

IJCAI International Joint Conference on Artificial Intelligence2017, Pages 3988-399426th International Joint Conference on Artificial Intelligence, IJCAI 2017; Melbourne; Australia; 19 August 2017 through 25 August 2017; Code 130864

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