Title: A Question Answering Approach to Emotion Cause Extraction,
Abstract: Emotion cause extraction aims to identify the reasons behind a certain
emotion expressed in text. It is a much more difficult task compared to emotion
classification. Inspired by recent advances in using deep memory networks for
question answering (QA), we propose a new approach which considers emotion
cause identification as a reading comprehension task in QA. Inspired by
convolutional neural networks, we propose a new mechanism to store relevant
context in different memory slots to model context information. Our proposed
approach can extract both word level sequence features and lexical features.
Performance evaluation shows that our method achieves the state-of-the-art
performance on a recently released emotion cause dataset, outperforming a
number of competitive baselines by at least 3.01% in F-measure.