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| Extracting Name Entities from Ecological Restoration Literature with Bi-LSTM+CRF |
Ma Jianxia( ),Yuan Hui,Jiang Xiang |
The Northwest Institute of Eco-Environment and Resources, Library and Information Center, Chinese Academy of Sciences, Lanzhou 730000, China
Department of Library, Information and Archives Management, University of Chinese Academy of Sciences,Beijing 100190, China |
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Abstract [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.
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Received: 08 January 2019
Published: 26 April 2020
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Corresponding Authors:
Ma Jianxia
E-mail: majx@lzb.ac.cn
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