关键词: 智能问答系统, 知识图谱, 双向长短期记忆模型(Bi-LSTM), 条件随机场(CRF)

Abstract:  Aiming at the difficulty of entity recognition in agricultural Q & A system, this paper proposed an improved Bi-LSTM-CRF based entity recognition method. Firstly, the word vector based on context information was generated through the pretreatment of BERT pretraining model, and then the trained word vector was input into Bi-LSTM-CRF for further training processing. Finally, the entity recognition, entity query and agricultural knowledge answering subsystems in the agricultural field were designed using Pythons Django framework. After experimental comparison, the improved Bi-LSTM-CRF proposed in this paper had better entity recognition ability in the field of agricultural information, and the precision rate, the recall rate, and F1 value on the agricultural information corpus were 93.23%, 91.08% and 92.16%, respectively. This paper realized the knowledge map website demonstration of entity recognition and agricultural information question answering in the agricultural field, which was of great significance to the development of agricultural informatization.

Key words: intelligent question answering system, knowledge graph, bidirectional long and shortterm memory model (Bi-LSTM), conditional random field (CRF)