扩充语义维度的BiGRU-AM突发事件要素识别研究

Identifying Emergency Elements Based on BiGRU-AM Model with Extended Semantic Dimension

【目的】为了解决循环神经网络对于重要程度不同的信息特征可解释性差的问题,本文提出一种扩充语义维度的BiGRU-AM突发事件要素识别方法。【方法】首先将文本语料训练为词向量,并将生成的词向量联接词性、依存句法关系等语义特征;然后通过BiGRU模型提取上下文信息特征,将注意力机制引入BiGRU网络,使得特征的提取更有选择性;最后将学习到的特征经过Softmax函数激活,输出识别结果。【结果】利用扩充了语义维度的BiGRU-AM模型在CEC数据集中进行实验,仿真实验结果表明,本文方法相较于其他浅层机器学习算法,F值提升了2%~21%不等。【局限】在判断语义关系方面较为局限;语料预处理的过程中依赖分词工具的准确性;超参数只是单方面的依序确定,缺乏关联性;F值的提升使得预处理工作的开销增大。【结论】扩充语义维度的BiGRU-AM模型可以有效地处理突发事件要素识别任务。

[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.