Abstract: Aiming at various unruly behavior of civil aviation passengers, such as making calls and disturbing other passengers, a Tag+Bi-LSTM+CRF neural network model is proposed to obtain the identity of unruly passengers. Considering that there are multiple entities in a sentence in a civil aviation text record, the pattern of entities appearing in the sentence may contain useful semantic information. The characters in the named entity recognition are marked with the BIOES method and then connected in series with word embedding and position embedding for the purpose of enriching input expressions. First, use the Yedda tool to label the entities at random in record text of civil aviation passengers, and combine word embedding and location embedding as input to the model. Second, the bi-directional long short-term memory(Bi-LSTM) model is used to obtain the contextual features of the sequence text. Then, the sequence labeling result is obtained through the conditional random field(CRF) model. Finally, a dropout layer is added into the input layer and Bi-LSTM layer to prevent overfitting. The experimental results show that the accuracy, recall rate and F1 of the unruly passenger identification are as high as 96% or more, and it can effectively obtain information of the unruly passengers with regard to behavior, grade, punishment, and time limit.

Key words: namely entity recognition, long short-term memory(LSTM), conditional random field(CRF), unruly passengers