Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages
Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages
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DOI：
10.1080/13658816.2023.2266495
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发表时间：
2023-10
影响因子：
5.7
通讯作者：
Yingjie Hu;Gengchen Mai;Chris Cundy;Kristy Choi;Ni Lao;Wei Liu;Gaurish Lakhanpal;Ryan Zhenqi Zhou;Kenneth Joseph
中科院分区：
文献类型：
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作者：
Yingjie Hu;Gengchen Mai;Chris Cundy;Kristy Choi;Ni Lao;Wei Liu;Gaurish Lakhanpal;Ryan Zhenqi Zhou;Kenneth Joseph
摘要人们在自然灾害期间发布的社交媒体消息往往包含重要的位置描述，如受害者的位置。最近的研究表明，这些位置描述中的许多都超出了简单的地名，如城市名称和街道名称，并且很难使用典型的命名实体识别(NER)工具来提取。虽然可以训练高级机器学习模型，但它们需要大量标记的训练数据集，创建这些数据集可能既耗时又费力。在这项工作中，我们提出了一种融合位置描述的地理知识和生成式预训练变压器(GPT)模型的方法，如ChatGPT和GPT-4。其结果是一个以地理知识为导向的GPT模型，可以从与灾难相关的社交媒体消息中准确地提取位置描述。此外，我们的方法只使用了22个编码地理知识的训练样本。我们在飓风哈维的推文数据集上进行了实验，将该方法与九种替代方法进行了比较。我们的方法比通常使用的NER方法提高了40%以上。实验结果还表明，地理知识对于指导GPT模型的行为是不可或缺的。提取的位置描述可以帮助灾难响应人员更快地到达灾民手中，甚至可能拯救生命。
Abstract 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.