Deep Learning Approach for Intelligent Named Entity Recognition of Cyber Security
Table 1: Average weighted performance metrics for all entity types.
['[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-score']
The training dataset was used to train the models and these trained models were tested using the testing data set. LSTM, RNN, GRU, Bidirectional GRU contains 128 hidden units. Embedding dimension of 128, learning rate of 0.005, and Stochastic Gradient Descent (SGD) learning method are used in all the experiments. The performance metrics in terms of precision, recall, and f1-score shows that Bidirectional GRU+CNN-CRF outperformed all other architectures.