%A Ma Jianxia,Yuan Hui,Jiang Xiang %T Extracting Name Entities from Ecological Restoration Literature with Bi-LSTM+CRF %0 Journal Article %D 2020 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2019.0034 %P 78-88 %V 4 %N 2/3 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4796.shtml} %8 2020-02-25 %X

[Objective] This study tries to extract named entities from the text, such as fragile ecological governance technology, implementation site, and implementation time, etc.[Methods] We combined the Bi-LSTM+CRF and feature-based named entity knowledge base to automatically extract needed data from CNKI documents.[Results] For the extraction of entities on ecological governance technology, the P, R and F1 values were 74.34%, 64.04% and 68.81%, respectively. Compared to the classic CRF method, our new model improves the P and F1 values by 9.41% and 4.26%, while the R value was basically the same.[Limitations] The accuracy of Chinese word segmentation tools may affect the performance of our model. More research is needed to study the relationship among entities.[Conclusions] The proposed model could be used for resource and environment information analysis based on fine-grained contents.