Biomedical Named Entity Recognition based on Long and Short Term Memory Model

In view of the problem of biological entity recognition, this paper proposes an improved Long Short-Term Memory (LSTM) recognition method based on the improved bidirectional long and short term memory model. First of all, based on the improvement of re constructed corpus is used to solve the imbalance problem in the distribution of biological entities data sampling algorithm; then, by coupling the forgotten and the input threshold combination to improve the LSTM memory unit, update method to choose the reasonable use of forgotten door control unit state in memory left information improve the biological entity recognition effect. Finally, the test was carried out on the JNLPBA 2004 corpus, and the accuracy rate of 79.7% and the value of 74.1% of the F were obtained. Experimental results show that the proposed recognition method not only has better generalization ability without external assistance, but also effectively improves the recognition effect of biological entities.