Joint Extraction of Multiple Relations and Entities by using a Hybrid Neural Network
Peng Zhou1,2; Suncong Zheng1,2; Jiaming Xu1; Zhenyu Qi1; Hongyun Bao1; Bo Xu1,2
2017
会议名称In Proceedings of the 16th China National Conference on Computational Linguistics (CCL2017)
页码135-146
会议日期2017/10/13-2017/10/15
会议地点Nanjing, China
摘要
This paper proposes a novel end-to-end neural model to jointly extract entities and relations in a sentence. Unlike most existing approaches, the proposed model uses a hybrid neural network to automatically learn sentence features and does not rely on any Natural Language Processing (NLP) tools, such as dependency parser. Our model is further capable of modeling multiple relations and their corresponding entity pairs simultaneously. Experiments on the CoNLL04 dataset demonstrate that our model using only word embeddings as input features achieves state-of-the-art performance.
 
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19658
专题数字内容技术与服务研究中心_听觉模型与认知计算
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Peng Zhou,Suncong Zheng,Jiaming Xu,et al. Joint Extraction of Multiple Relations and Entities by using a Hybrid Neural Network[C],2017:135-146.
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