Extraction of Malignant Tumor Diagnostic Text Information Based on Named Entity Recognition

In order to extract named entity recognition from malignant tumor medical medical text data, an algorithm that combines the bidirectional long short-term memory network and the conditional random field (Bi-LSTM-CRF) is proposed. This method adds the CRF layer processing after Bi-LSTM network output, so that the model has better comprehensive performance since the CRF can take orders of words into consideration. The experimental results show that comparing to the algorithm that combines the maximum entropy Markov model and the conditional random field (MEMM+CRF) and the algorithm of the bidirectional long short-term memory network (Bi-LSTM), our method is more excellent in entity recognition for comprehensive practical applications, and can basically identify the corresponding medical entity.