从文本中提取受害者计数
Extracting Victim Counts from Text
摘要：
在危机事件期间，人道主义部门的决策者依靠及时和准确的信息。了解有多少平民在地震中受伤对于正确分配援助至关重要。有关这类受害者人数的信息往往只能在报纸和其他报道的事件描述全文中获得。从文本中提取数字具有挑战性：数字有不同的格式，可能需要进行数字推理。这使得纯粹基于字符串匹配的方法是不够的。因此，除了死亡人数之外，受伤、流离失所或受虐待的受害者的细粒度计数往往无法提取，也无法看到。我们将受害者计数提取作为具有回归或分类目标的问答（QA）任务。我们比较正则表达式，依存句法分析，语义角色标注为基础的方法，和先进的文本到文本模型。除了模型的准确性，我们还分析了提取的可靠性和鲁棒性，这是这项敏感任务的关键。特别是，我们讨论模型校准和调查Few-Shot和分布性能。最后，我们提出了一个全面的建议，该模型选择不同的desiderata和数据域。我们的工作是第一批在现实世界的用例中应用以数字为中心的大语言模型并产生积极影响的工作。
英文摘要：
Decision-makers in the humanitarian sector rely on timely and exact information during crisis events. Knowing how many civilians were injured during an earthquake is vital to allocate aids properly. Information about such victim counts is often only available within full-text event descriptions from newspapers and other reports. Extracting numbers from text is challenging: numbers have different formats and may require numeric reasoning. This renders purely string matching-based approaches insufficient. As a consequence, fine-grained counts of injured, displaced, or abused victims beyond fatalities are often not extracted and remain unseen. We cast victim count extraction as a question answering (QA) task with a regression or classification objective. We compare regex, dependency parsing, semantic role labeling-based approaches, and advanced text-to-text models. Beyond model accuracy, we analyze extraction reliability and robustness which are key for this sensitive task. In particular, we discuss model calibration and investigate few-shot and out-of-distribution performance. Ultimately, we make a comprehensive recommendation on which model to select for different desiderata and data domains. Our work is among the first to apply numeracy-focused large language models in a real-world use case with a positive impact.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
