A Method for Chinese Entity Relationship Extraction Based on Bi-GRU

DOI

10.2991/masta-19.2019.9How to use a DOI?

Keywords

Bi-GRU, Regional list embedding(RLE), Hybrid neural network

Abstract

In order to solve some defects of single deep neural network in Chinese entity Relationship Extraction task, a hybrid neural network entity relationship extraction model is designed and implemented in this paper. The model combines convolution network and bidirectional GRU model with a unified architecture, by defining varisized regional list embedding, it produces nonobjective feature representations of word vectors in distinction positions, and it has only Chinese character vectors and Chinese character word vectors, without position embedding. The laboratory findings show that our method is very effective on the Chinese corpus ACE2005 dataset about entities extraction task.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jian-qiong Xiao
AU  - Zhi-yong Zhou
AU  - Xing-xian Luo
PY  - 2019/07
DA  - 2019/07
TI  - A Method for Chinese Entity Relationship Extraction Based on Bi-GRU
BT  - Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
PB  - Atlantis Press
SP  - 54
EP  - 57
SN  - 1951-6851
UR  - https://doi.org/10.2991/masta-19.2019.9
DO  - 10.2991/masta-19.2019.9
ID  - Xiao2019/07
ER  -