Abstract
This work presents an Argument Mining process that extracts argumentative entities from clinical texts and identifies their relationships using token classification and Natural Language Inference techniques. Compared to straightforward methods like text classification, this methodology demonstrates superior performance in data-scarce settings. By assessing the effectiveness of these methods in identifying argumentative structures that support or refute possible diagnoses, this research lays the groundwork for future tools that can provide evidence-based justifications for machine-generated clinical conclusions.
Abstract (translated)
这项工作提出了一种论点挖掘过程，该过程从临床文本中提取论据实体，并利用词元分类和自然语言推理技术来识别它们之间的关系。与诸如文本分类之类的直接方法相比，这种研究方法在数据不足的情况下表现出色。通过评估这些方法在识别支持或反驳可能诊断的论证结构方面的有效性，这项研究为未来能够提供基于证据的理由以支持机器生成的临床结论的工具奠定了基础。
URL
https://arxiv.org/abs/2506.12823
