An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMs  

作　　者：Ruifeng Xu Jiannan Hu Qin Lu Dongyin Wu Lin Gui 

机构地区：[1]School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School [2]Department of Computing, the Hong Kong Polytechnic University

出　　处：《Tsinghua Science and Technology》2017年第6期646-659,共14页清华大学学报（自然科学版（英文版）

基　　金：supported by the National Natural Science Foundation of China(Nos.61370165,U1636103,and 61632011);Shenzhen Foundational Research Funding(Nos.JCYJ20150625142543470 and JCYJ20170307150024907);Guangdong Provincial Engineering Technology Research Center for Data Science(No.2016KF09)

摘　　要：In this paper, we present a new challenging task for emotion analysis, namely emotion cause extraction.In this task, we focus on the detection of emotion cause a.k.a the reason or the stimulant of an emotion, rather than the regular emotion classification or emotion component extraction. Since there is no open dataset for this task available, we first designed and annotated an emotion cause dataset which follows the scheme of W3 C Emotion Markup Language. We then present an emotion cause detection method by using event extraction framework,where a tree structure-based representation method is used to represent the events. Since the distribution of events is imbalanced in the training data, we propose an under-sampling-based bagging algorithm to solve this problem. Even with a limited training set, the proposed approach may still extract sufficient features for analysis by a bagging of multi-kernel based SVMs method. Evaluations show that our approach achieves an F-measure 7.04%higher than the state-of-the-art methods.In this paper, we present a new challenging task for emotion analysis, namely emotion cause extraction.In this task, we focus on the detection of emotion cause a.k.a the reason or the stimulant of an emotion, rather than the regular emotion classification or emotion component extraction. Since there is no open dataset for this task available, we first designed and annotated an emotion cause dataset which follows the scheme of W3 C Emotion Markup Language. We then present an emotion cause detection method by using event extraction framework,where a tree structure-based representation method is used to represent the events. Since the distribution of events is imbalanced in the training data, we propose an under-sampling-based bagging algorithm to solve this problem. Even with a limited training set, the proposed approach may still extract sufficient features for analysis by a bagging of multi-kernel based SVMs method. Evaluations show that our approach achieves an F-measure 7.04%higher than the state-of-the-art methods.

关 键 词：emotion cause detection event extraction multi-kernel SVMs bagging 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]