Event Extraction Methods for Government Hotline Domains

Event extraction for government hotlines is crucial for improving scheduling efficiency and optimizing resource allocation, but varying text lengths and diverse event types increase the extraction difficulty. To this end, we propose a method that fuses the spanning mechanism with machine reading comprehension to first extract trigger words and predict event types, and then extract event arguments using customized problem templates. Experimental results show that the method outperforms traditional methods in terms of precision, recall, and F1 value, and can effectively identify public service demands and emergency events, providing support for government information integration and decision optimization.