Biomedical Named Entity Recognition based on Long and Short Term Memory Model
Youliang Huang, Sajid Ali, Li Wang, Renquan Liu
Available Online December 2017.
- https://doi.org/10.2991/mcei-17.2017.113How to use a DOI?
- Long and short term memory model; Literature mining; Biomedical naming recognition; Neural network
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Youliang Huang AU - Sajid Ali AU - Li Wang AU - Renquan Liu PY - 2017/12 DA - 2017/12 TI - Biomedical Named Entity Recognition based on Long and Short Term Memory Model BT - 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/mcei-17.2017.113 DO - https://doi.org/10.2991/mcei-17.2017.113 ID - Huang2017/12 ER -