Cyber security named entity recognition based on deep active learning

Abstract:

To solve the problem of low accuracy in general cyber security named entity recognition (NER) model，a deep active learning method is proposed for NER in general cyber security field, which is based on character feature，BiLSTM and conditional random field (CRF). The neural network model is for sequence labeling and CRF is for label dependency constraint，which then improves the accuracy of sequence labeling. Furthermore，as for datasets with the insufficient labeled samples in cyber security field，the proposed active learning method is able to achieve better sequence labeling effect with a small number of labeled samples.