Schema-free information extraction method based on dynamic structure generation from text content

Text data are difficult for computers to process directly due to their unstructured nature. In traditional text information extraction systems, predefined structural templates are typically used to extract structured data, such as event schema or attribute-entity pairs. However, such template-based designs limit the adaptability of models when dealing with unseen domains and weaken their ability to capture the complex semantics of natural language. In this study, we propose a deep learning-driven schema-free structured information extraction paradigm to eliminate the dependence on manually designed templates. Unlike traditional slot-filling methods, our framework first extracts entities appearing in unstructured text, and then employs a deep neural text generation model to dynamically infer their semantic roles or structured attributes based on contextual semantics. This paradigm provides a unified and data-driven approach to representing unstructured text in a structured form, and can be effectively applied to complex event extraction tasks. Comprehensive experiments conducted on two benchmark datasets demonstrate that the proposed deep learning-based method not only achieves higher accuracy than the baseline models, but also significantly alleviates information omission and semantic fragmentation, exhibiting strong generalisation and robustness.

Authors

• Guanghui Chang
• Ronghui Zhang School of Intelligent Systems Engineering, Sun Yat-sen University
• Kuo Chen
• Yongxin Ge

Keywords

• Schema-free information extraction
• Event extraction
• Deep learning
• Text generation model