  Social media messages posted by people during natural disasters often contain
important location descriptions, such as the locations of victims. Recent
research has shown that many of these location descriptions go beyond simple
place names, such as city names and street names, and are difficult to extract
using typical named entity recognition (NER) tools. While advanced machine
learning models could be trained, they require large labeled training datasets
that can be time-consuming and labor-intensive to create. In this work, we
propose a method that fuses geo-knowledge of location descriptions and a
Generative Pre-trained Transformer (GPT) model, such as ChatGPT and GPT-4. The
result is a geo-knowledge-guided GPT model that can accurately extract location
descriptions from disaster-related social media messages. Also, only 22
training examples encoding geo-knowledge are used in our method. We conduct
experiments to compare this method with nine alternative approaches on a
dataset of tweets from Hurricane Harvey. Our method demonstrates an over 40%
improvement over typically used NER approaches. The experiment results also
show that geo-knowledge is indispensable for guiding the behavior of GPT
models. The extracted location descriptions can help disaster responders reach
victims more quickly and may even save lives.
