计算机科学与探索2019，Vol.13Issue(12)：2085-2093,9.DOI:10.3778/j.issn.1673-9418.1902010
植物属性文本的命名实体识别方法研究
Research on Named Entity Recognition Method in Plant Attribute Text
摘要
Abstract
Named entity recognition of plant attribute texts plays a significant role in the information extraction and the construction of knowledge graph in the field of forestry. This paper proposes a named entity recognition method BCC-P (BiLSTM-CNN-CRF model in plant), which is based on bi-directional long short term memory (BiLSTM) model, convolutional neural network (CNN) model, and conditional random fields (CRF) model. This paper analyzes the characteristics of plant attribute texts, does the work of pre-processing and labeling, and constructs a dataset. The BCC-P method can effectively extract the context features in plant attribute texts by modeling the input texts with BiLSTM model. Furthermore, the obtained features are transferred to the CNN model to further extract the implicit feature. Finally, the CRF model is used to label plant attribute texts, and the optimal label result on the sentence sequence is output. The experiment on plant attribute texts shows that, the accuracy of BCC-P method achieves 91.8%. Therefore, BCC-P method can be effectively applied to named entity recognition in plant attribute texts.关键词
命名实体识别/双向长短时记忆网络(BiLSTM)/卷积神经网络(CNN)/条件随机场(CRF)Key words
named entity recognition/bi-directional long short term memory (BiLSTM)/convolutional neural net-work (CNN)/conditional random fields (CRF)分类
信息技术与安全科学引用本文复制引用
李冬梅,檀稳..植物属性文本的命名实体识别方法研究[J].计算机科学与探索,2019,13(12):2085-2093,9.基金项目
The National Natural Science Foundation of China under Grant No.61602042 (国家自然科学基金) （国家自然科学基金）
the Fundamental Research Funds for the Central Universities of China under Grant No.TD2014-02 (中央高校基本科研业务费专项资金). （中央高校基本科研业务费专项资金）
