NOTAM Text Analysis and Classification Based on Attention Mechanism

As one of the important carriers, Notice to Air Men (NOTAM) provides key aeronautical security information during flight operation. However, a large number of NOTAM texts lack systematic utilization and organization. To improve the application of NOTAM texts, the Natural language processing can be used to classify and analyze NOTAM texts, and their effective processing attaches significance to monitor the air security. In this paper, a corpus set of NOTAM by web crawler technology has been built to generate raw data. Based on the aeronautical intelligence knowledge system, the corpus set is systematically pre-processed and analyzed, while the key information in NOTAM and corpus features are also extracted. Considering the high-dimensional and sparse of corpus set, the traditional bi-directional Recurrent Neural Network (Bi-RNN) architecture is improved by adding an attention layer which not only allows the Bi-RNN to capture hidden dependency to improve sparse features, but also allows the attention mechanism to weigh the key information base on their perceived importance to optimize text representation. Experiments show that that the Attention-based BiRNN network outperforms the other four models, achieving a precision of 94.17% and an F1-Score of 93.66%. The model rapidly increases the utilization of text features meanwhile reduces the loss, which demonstrates it is a useful tool for the task of NOTAM texts classification and air security surveillance.