This study explores how deep learning, specifically transformer networks like BERTurk, can extract critical information from social media texts for disaster response. A total of 310 earthquake-related tweets were manually annotated to identify entities such as location, request, supply, status, and time, and to extract relationships between them. The findings highlight the potential of NLP for accelerating and improving emergency management efforts during disasters.