结合Word2vec和BiLSTM的民航非计划事件分析方法
A civil aviation unplanned event analysis method combined with Word2vec and BiLSTM
王捷 1周迪 1左洪福 1黄维2
作者信息
- 1. 南京航空航天大学民航学院,江苏南京 211106
- 2. 成都天奥电子股份有限公司,四川成都 611731
- 折叠
摘要
安全是民航业的核心主题.针对目前民航非计划事件分析严重依赖专家经验及分析效率低下的问题,文章提出一种结合Word2vec和双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络模型的民航非计划事件分析方法.首先采用Word2vec模型针对事件文本语料进行词向量训练,缩小空间向量维度;然后通过BiLSTM模型自动提取特征,获取事件文本的完整序列信息和上下文特征向量;最后采用softmax函数对民航非计划事件进行分类.实验结果表明,所提出的方法分类效果更好,能达到更优的准确率和月值,对不平衡数据样本同样具有较稳定的分类性能,证明了该方法在民航非计划事件分析上的适用性和有效性.
Abstract
Safety is the core theme of the civil aviation industry.Aiming at the problem that the analy-sis of civil aviation unplanned events heavily depends on expert experience and the low analysis effi-ciency,a civil aviation unplanned event analysis method is proposed,which combines Word2vec and bidirectional long short-term memory(BiLSTM)neural network model.Firstly,Word2vec is used to train word vectors for event text corpus,reducing the dimension of the space vector,Then,the fea-tures are automatically extracted by BiLSTM model to obtain the complete sequence information and context feature vector of the event text.Finally,the softmax function is used to classify civil aviation unplanned events.The experimental results show that the proposed method has better classification effect and can achieve better accuracy and F1 value,and also has more stable classification performance for unbalanced data samples,which proves the applicability and effectiveness of this method in the a-nalysis of civil aviation unplanned events.
关键词
民航安全/文本分析/非计划事件/Word2vec/双向长短期记忆(BiLSTM)神经网络Key words
civil aviation safety/text analysis/unplanned event/Word2vec/bidirectional long short-term memory(BiLSTM)neural network引用本文复制引用
基金项目
国家自然科学基金联合基金资助项目(U1933202)
民航大NSF重点基金资助项目(U1733201)
出版年
2024
NETL
NSTL
万方数据