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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (9): 91-99    DOI: 10.11925/infotech.2096-3467.2020.0022
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Identifying Emergency Elements Based on BiGRU-AM Model with Extended Semantic Dimension
Yin Haoran,Cao Jinxuan(),Cao Luzhe,Wang Guodong
College of Information Cyber Security, People’s Public Security University of China, Beijing 100035, China
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[Objective] This paper proposes a new method to recognize emergency elements based on a modified BiGRU-AM model, aiming to improve the poor interpretation of recurrent neural networks for information features with different degrees of importance.[Methods] First, we trained the text corpus to create word vectors, which were connected to semantic features like dependent syntactic relations. Then, we extracted contextual information features with BiGRU. We also introduced attention mechanism to the BiGRU network to extract diversified features. Finally, we activated the learned features with softmax function to generate needed elements.[Results] We examined the modified BiGRU-AM model with the CEC dataset and found its F-value was 2%-21% higher than algorithms of shallow machine learning.[Limitations] The proposed model’s ablilty to decide semantic relations, the accuracy of word segmentation tool, and the hyper parameters need to be improved.[Conclusions] The BiGRU-AM model with extended semantic dimension could effectively extract emergency elements.

Key wordsEmergency      Event Element Recognition      Recurrent Neural Network      Attention Mechanism     
Received: 02 January 2020      Published: 17 July 2020
ZTFLH:  TP183  
Corresponding Authors: Cao Jinxuan     E-mail:

Cite this article:

Yin Haoran,Cao Jinxuan,Cao Luzhe,Wang Guodong. Identifying Emergency Elements Based on BiGRU-AM Model with Extended Semantic Dimension. Data Analysis and Knowledge Discovery, 2020, 4(9): 91-99.

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Event Element Recognition Model
Unit Structure of GRU
事件类型 事件数量 句子数量 时间要素 地点要素 对象要素
地震 62 401 463 487 1 072
火灾 75 433 531 726 978
交通事故 85 514 697 1031 1 509
恐怖袭击 49 324 431 412 674
食物中毒 61 392 637 376 943
合计 332 2 064 2 759 3 032 5 176
CEC Corpus
维度 精确率/% 召回率/% F值/%
50 69.30 70.05 69.67
100 73.36 71.77 72.56
150 77.12 73.14 75.08
200 74.21 72.16 73.17
250 68.61 66.27 67.42
300 70.68 66.83 68.70
Recognition Effect of Different Word Vector Dimensions
Recognition Effect of Different Dropout Value
Recognition Effect of Different Epoch Value
实验方法 精确率/% 召回率/% F值/%
POS[20] 68.80 47.70 56.34
触发词拓展[21] 65.28 63.49 64.37
BiGRU 74.59 71.20 72.86
依存句法分析[22] 72.67 73.71 73.19
MaxEnt+规则统计[23] 68.10 83.00 74.80
BiGRU-AM 76.29 73.63 74.93
要素填充+CRF[24] 79.40 73.70 76.00
ESD-BiGRU-AM 79.12 76.83 77.96
Experimental Results
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