Joint Entity Relation Extraction Based on LSTM via Attention Mechanism

Entity relation extraction holds a significant role in extracting structured information from unstructured text, serving as a foundational component for various other tasks within natural language processing. The pipeline method in entity relation extraction separates entity subtask from relation subtask, causing an error propagation. Contemporary researchers are more inclined to amalgamate two subtasks, improve and innovate the structures of models to carry out joint entity relation extraction. However, these models often merely capture surface-level text features, overlooking the profound-level semantics and syntax inherent within sentences, consequently forfeiting valuable knowledge. In this condition, we propose a joint entity relation extraction method that integrates context semantic and dependency syntax. The bidirectional long short-term memory network is employed to explore context semantic features of sentences, and tree-structured LSTM is utilized to extract dependency syntactic features, subsequently two types of features are fused with the attention mechanism for joint extraction. Experiment results demonstrate that compared with other models, the Accuracy, Recall and F1-value of our proposed method are increased evidently, proving that semantic and syntactic information contained in sentences are beneficial for entity relation extraction.