Open AccessChinese Emergency Event Recognition Based on BiGRU-AM Model
Author(s) -
Haoran Yin,
Jinxuan Cao,
Guodong Wang,
Houlu Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/3/032124
Subject(s) - softmax function , computer science , interpretability , artificial intelligence , event (particle physics) , pattern recognition (psychology) , feature (linguistics) , feature extraction , convolutional neural network , linguistics , philosophy , physics , quantum mechanics
A Chinese emergency event recognition based on bidirectional gated recurrent unit BiGRU-AM model with attention mechanism is proposed to resolve the limitation of traditional event recognition methods and the poor interpretability of general recurrent neural networks in respect of information features with different degrees of importance. Firstly, text corpus was trained to generate word vectors, and contextual information features were extracted through BiGRU, and then attention mechanism was introduced into BiGRU network to make feature extraction more selective. Finally, the learned features were activated by softmax function to output recognition results. Simulation results show that this method improves the accuracy and recall rate of emergency recognition, and the F value is superior to other methods.