Enhancing pre-trained language model by answering natural questions for event extraction
Yuxin Zhang, Qing Han · Frontiers in Artificial Intelligence · 2025
Introduction: Event extraction is the task of identifying and extracting structured information about events from unstructured text. However, event extraction remains challenging due to the complexity and diversity of event expressions, as well as the ambiguity and context dependency of language. Methods: In this paper, we propose a new method to improve the precision and recall of event extraction by including topic words related to events and their contexts, directing the model to focus on the relevant information, and filtering the noise. Results: This method was evaluated on the ACE 2005 dataset, achieving an F1-score of 77.27% with significant improvements in both precision and recall. Discussion: Our results show that the use of topic words and question answering techniques can effectively address the challenges faced by event extraction and pave the way for the development of more accurate and robust event extraction systems.