Entity Relationship Extraction Method Based on Multi-head Attention and Graph Convolutional Network

Extracting entities and relations from text is crucial in the field of natural language processing. Current methods for relation extraction rely on training sets labeled using remote supervision techniques. However, these methods have limitations as they do not consider the connection between entity and relation extraction and cannot extract overlapping entities and relations. Therefore, accurate joint entity-relation extraction remains challenging. Our paper introduces a model for entity relation extraction based on multi-head attention and graph convolutional networks. We utilize the multi-head attention approach to extract entity features, building on the text features extracted by the graph convolutional network. Utilizing the New York Times (NYT) dataset, we evaluated the performance of our model. The experimentation revealed that our model effectively captures the semantic correlation between entity and relation extraction and minimizes the impact of unrelated entity pairings, resulting in improved recognition accuracy even in scenarios with overlapping entities.