[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.
尹浩然,曹金璇,曹鲁喆,王国栋. 扩充语义维度的BiGRU-AM突发事件要素识别研究*[J]. 数据分析与知识发现, 2020, 4(9): 91-99.
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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