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）

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.

Key words: named entity recognition, bi-directional long short term memory (BiLSTM), convolutional neural net-work (CNN), conditional random fields (CRF)