基于CNN-BLSTM的食品舆情实体关系抽取模型研究

食品舆情实体关系抽取是构建食品舆情知识图谱的关键技术,也是当前信息抽取领域的重要研究课题。针对食品舆情中常出现的实体对多关系问题,在卷积神经网络(convolutional neural network,CNN)中引入基于位置感知的领域词语义注意力机制；在双向长短时记忆(bidirectional long short-term memory,BLSTM)网络中引入基于位置感知的语义角色注意力机制,构建基于CNN-BLSTM的食品舆情实体关系抽取模型。在食品舆情数据集上进行了对比实验,实验结果表明:基于CNN-BLSTM的食品舆情实体关系抽取模型在食品舆情数据集上准确率比常用的几种深度神经网络模型高出8.7%~13.94%,验证了模型的合理性和有效性。

Research on Entity Relationship Extraction Model of Food Public Opinion Based on CNN-BLSTM

The extraction of food public opinion entity relationship is the key technology for constructing the map of food sensation knowledge, and it is also an important research topic in the field of information extraction. Aiming at the entity-to-many relationship problem often appearing in food grievances, the location-aware domain-based semantic attention mechanism is introduced to the convolutional neural network (CNN) and location-aware semantic roles attention mechanism is introduced to the bidirectional long short-term memory network (BLSTM). Then, model was constructed based on CNN-BLSTM for food sentiment entity relationship extraction. In this paper, a comparative experiment was carried out on the food moisture dataset. The experimental results showed that the accuracy of the entity relationship extraction model of food public opinion based on CNN-BLSTM was higher than that of the commonly used deep neural network models. From 8.7% to 13.94%, the rationality and effectiveness of the proposed model were verified.