Emotion cause detection with enhanced-representation attention convolutional-context network

Emotion cause detection is mainly to identify the emotion cause with an emotion expression text, which plays a critical role in building NLP applications. This task is much more difficult than other emotion classification and emotion extraction problems. However, most existing methods only focus on partial information to extract the emotion cause. In this study, we present a new approach to combine the emotion word with its synonyms in order to discover the deep and semantic information by enhanced-representation and attention mechanism. Meanwhile, we propose a new mechanism to introduce the hierarchical context behind emotion word information for extracting emotion cause inspired by a convolution operation. Our proposed framework can extract both enhanced represented emotion level features and context level features to better detect the emotion cause. We have conducted extensive experiments on the emotion cause dataset. Experimental results demonstrate the effectiveness of our proposed model, outperforming a number of competitive baselines by at least 3.39% in F1-measure.