一个从法律文本中自动抽取语义法律元数据的框架
An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts
摘要：
语义法律元数据提供有助于理解和解释法律条款的信息。因此，这种元数据对于系统分析法律要求十分重要。然而，手动增强一个大型的法律语料库与语义元数据是昂贵的。我们的工作是出于两个观察：（1）现有的需求工程（RE）文献没有提供一个统一的视图语义元数据类型，是有用的法律需求分析;（2）对语义法律元数据提取的自动化支持很少，它没有充分利用人工智能技术的潜力，特别是自然语言处理（NLP）和机器学习（ML）。我们的目标是采取措施克服这些限制。要做到这一点，我们回顾和调和的语义法律元数据类型提出的RE文献。随后，我们设计了一个自动提取方法，使用NLP和ML识别的元数据类型。我们通过两个案例研究，对我们的方法进行评估。我们的研究结果表明，在生成元数据注释的准确性很高。特别是，在两个案例研究中，我们能够获得97.2%和82.4%的精确度分数和94.9%和92.4%的召回分数。
英文摘要：
Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97.2% and 82.4% and recall scores of 94.9% and 92.4%.
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。
