Abstract
Emotion cause detection which recognizes the cause of an emotion in microblogs is a challenging research issue in Natural Language Processing field. In this paper, we propose a hierarchical Convolution Neural Network (Hier-CNN) for emotion cause detection. Our Hier-CNN model deals with the feature sparse problem through a clause-level encoder, and handles the less event-based information problem by a subtweet-level encoder. In the clause-level encoder, the representation of a word is augmented with its context. In the subtweet-level encoder, the event-based features are extracted in term of microblogs. Experimental results show that our model outperforms several strong baselines and achieves the state-of-the-art performance.
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Chen, Y., Hou, W., Cheng, X. (2018). Hierarchical Convolution Neural Network for Emotion Cause Detection on Microblogs. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_12
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