Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 1
135-139,157

Sematic relation classification model via hierarchical recurrent neural network

Hao Zhifeng1,2
Chen Peihui3
Cai Ruichu1
Wen Wen1
Wang Lijuan1
1. Faculty of Computer, Guangdong University of Technology, Guangzhou 510006, China
2. School of Mathematics & Big Data, Foshan University, Foshan Guangdong 528000, China
3. Dept. of Information Engineering, Shanwei Polytechnic, Shanwei Guangdong 516600, China
DOI: 10.19734/j.issn.1001-3695.2018.06.0461

Abstract

The method based on recurrent neural network combined with syntactic structure is widely used in relation classification, and the neural network is used to automatically acquire features and realize relation classification. However, the existing methods are mainly based on a single specific syntactic structure model, and the model of a specific syntactic structure cannot be transferred to other types of syntactic structures. Aiming at this problem, this paper proposed a hierarchical recurrent neural network model with multi-syntactic structure. It divided the hierarchical recurrent neural network into two layers for network construction. Firstly, it performed entity pre-training in the sequence layer, and used the Bi-LSTM-CRF fusion attention mechanism to improve the model's attention to the entity information on the text sequence, thereby obtaining more accurate. The more accurate entity feature information promoted better classification in the relation layer stage. Secondly, in the relation layer, the Bi-Tree-LSTM was nested above the sequence layer, and passed the hidden state and entity feature information of the sequence layer into the relation layer, then it weighted learned three different syntax structures using the shared parameters and classified the semantic relation finally. The experimental results show that the model has a marco-F1 value of 85.9% on the SemEval-2010 Task8 corpus, and further improves the robustness of the model.

Foundation Support

NSFC-广东联合基金资助项目(U1501254)
广东省自然科学基金资助项目(2014A030306004,2014A030308008)
广东省科技计划资助项目(2015B010108006,2015B010131015)
广东特支计划资助项目(2015TQ01X140)
广州市珠江科技新星资助项目(201610010101)
广州市科技计划资助项目(201604016075)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.06.0461
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 1
Section: Algorithm Research & Explore
Pages: 135-139,157
Serial Number: 1001-3695(2020)01-028-0135-05

Publish History

[2020-01-05] Printed Article

Cite This Article

郝志峰, 陈培辉, 蔡瑞初, 等. 基于叠层循环神经网络的语义关系分类模型 [J]. 计算机应用研究, 2020, 37 (1): 135-139,157. (Hao Zhifeng, Chen Peihui, Cai Ruichu, et al. Sematic relation classification model via hierarchical recurrent neural network [J]. Application Research of Computers, 2020, 37 (1): 135-139,157. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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