CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
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
Conference NameIn Proceedings of the 16th China National Conference on Computational Linguistics (CCL2017)
Pages135-146
Conference Date2017/10/13-2017/10/15
Conference PlaceNanjing, China
Abstract
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.
 
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19658
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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