Paper Info
Title
A Question Answering Approach for 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.
Year
DOI
Venue
2017
10.18653/v1/D17-1167
empirical methods in natural language processing
Field
DocType
Volume
Question answering,Computer science,Reading comprehension,Convolutional neural network,Emotion classification,Speech recognition,Artificial intelligence,Natural language processing,Machine learning
Conference
abs/1708.05482
Citations 
PageRank 
References 
10
0.57
19
Authors
6
Name
Order
Citations
PageRank
Lin Gui19412.82
Jiannan Hu2100.57
Yulan He31934123.88
Xu Ruifeng443253.04
Qin Lu568966.45
Du Jiachen6369.02