邱盈盈,洪宇,周文瑄,姚建民,朱巧明. 面向事件抽取的深度与主动联合学习方法[J]. 中文信息学报, 2018, 32(6): 98-106.
QIU Yingying, HONG Yu, ZHOU Wenxuan, YAO Jianmin, ZHU Qiaoming. Combining Deep Learning and Active Learning for Event Extraction. , 2018, 32(6): 98-106.
面向事件抽取的深度与主动联合学习方法
邱盈盈,洪宇,周文瑄,姚建民,朱巧明
苏州大学 江苏省计算机信息处理重点实验室,江苏 苏州 215006
Combining Deep Learning and Active Learning for Event Extraction
QIU Yingying, HONG Yu, ZHOU Wenxuan, YAO Jianmin, ZHU Qiaoming
Provincial Key Laboratory of Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu 215006, China
Abstract：Event extraction aims at extracting event information from raw texts and representing them as a structured text. As a basic event extraction method,supervised learning often suffers from small scale,imbalanced distribution and uneven quality of training corpus. Moreover,traditional event extraction methods based on feature engineering are complicated and will always cause error propagation. To address these issues,this paper presents a method to combine deep learning and active learning by the confidence of the query function based on RNN's trigger classification,in order to improve the quality and efficiency of corpus annotation as well as the ultimate performance. The experimental results show that this joint learning method can improve the event extraction,with substantial room for further exploration.
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2018, Vol. 32 
