CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
A Novel End2End Multiple Tagging Model for Knowledge Extraction
Yuanhua Song1,2; Hongyun Bao1; Zhineng Chen1; Jianquan Ouyang2
Conference NameIJCNN 2019
Conference Date2019-7-14
Conference PlaceBudapest, Hungary

It is an emerging research topic in NLP to joint extraction of knowledge including entities and relations from unstructured text and representing them as meaningful triplets. Despite significant progresses made by recent deep neural network based solutions, these methods still confront the overlapping issue that different relational triplets may have overlapped entities in a sentence, and it is troublesome to address this issue by current solutions. In this paper, we propose a novel end2end multiple tagging model to address the overlapping issue and extract knowledge from unstructured text. Specifically, we devise a multiple tagging scheme that transforms the problem of joint entity and relation extraction into a multiple sequence tagging problem. By using GRU as the building block for encoding-decoding, the proposed model is capable of handling the triplet overlapping problem because the decoder layer allows one entity to take part in more than one triplet. The whole network is end2end trainable and outputs all triplets in a sentence directly. Experimental results on the NYT and KBP benchmarks demonstrate that the proposed model significantly improves the recall of triplet, and consequently, achieving the new state-of-the-art in the task of triplet extraction.

MOST Discipline Catalogue工学::计算机科学与技术(可授工学、理学学位)
Indexed ByEI
Document Type会议论文
Affiliation1.Institute of Automation, Chinese Academy of Sciences Beijing, China
2.Xiangtan University
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yuanhua Song,Hongyun Bao,Zhineng Chen,et al. A Novel End2End Multiple Tagging Model for Knowledge Extraction[C],2019.
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