A Entity Relation Extraction Model with Enhanced Position Attention in Food Domain

摘要

Entity-relationship extraction is a fine-grained task for constructing a knowledge graph of food public opinion in the field of food public opinion, and it is also an important research topic in the field of current information extraction. This paper aims at the multi-entity-to-relationship problem that often occurs in food public opinion, the entity-relationship types are extracted from the BERT (Bidirectional Encoder Representation from Transformers) network model; In the bidirectional long short-term memory network (BLSTM), the entity-relationship types extracted by BERT model are integrated, and the semantic role attention mechanism based on position awareness is introduced to construct a model BERT-BLSTM-based entity-relationship extraction model for food public opinion at the same time. In this paper, comparative experiments were conducted on the food sentiment data set. The experimental results show that the accuracy of the BERT-BLSTM-based food sentiment entity-relationship extraction model proposed in this paper is 8.7 ~ 13.94% higher than several commonly used deep neural network models on the food sentiment data set, which verifies the rationality and effectiveness of the model proposed in this paper.