Joint Information Extraction Model Based on Feature Sharing

Abstract

To address the challenge of efficiently and accurately extracting entities, relationships, and events from unstructured text, a joint information extraction model based on feature sharing is proposed. This model utilizes the contextual information of entities, relationships, and events, and integrates entity extraction, relationship extraction, and event extraction tasks through a multi-feature cascade encoder to achieve joint extraction. To validate the effectiveness of the model, comparative analysis was conducted on military news datasets, comparing against two typical information extraction models. Results demonstrated superiority over current state-of-the-art baselines.