We apply NER to a particular sub-genre of legal texts in German: the genre of legal norms regulating administrative processes in public service administration. The analysis of such texts involves identifying stretches of text that instantiate one of ten classes identified by public service administration professionals. We investigate and compare three methods for performing Named Entity Recognition (NER) to detect these classes: a Rule-based system, deep discriminative models, and a deep generative model. Our results show that Deep Discriminative models outperform both the Rule-based system as well as the Deep Generative model, the latter two roughly performing equally well, outperforming each other in different classes. The main cause for this somewhat surprising result is arguably the fact that the classes used in the analysis are semantically and syntactically heterogeneous, in contrast to the classes used in more standard NER tasks. Deep Discriminative models appear to be better equipped for dealing with this heterogenerity than both generic LLMs and human linguists designing rule-based NER systems.
CDJUR-BR -- A Golden Collection of Legal Document from Brazilian Justice with Fine-Grained Named Entities
Universidade de Fortaleza（福塔莱萨大学）；Instituto Federal de Educação, Ciência e Tecnologia do Ceará（塞阿拉联邦教育、科学与技术学院）；Tribunal de Justiça do Estado do Ceará（塞阿拉州法院）
Named entity recognition for Serbian legal documents: Design, methodology and dataset development
The Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研究与发展研究所）；Faculty of Technical Sciences, University of Novi Sad（诺威萨大学技术科学学院）
Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network
Shandong University（山东大学）；Alibaba Group（阿里巴巴集团）；Indiana University Bloomington（印第安纳大学伯明顿分校）；University of Amsterdam（阿姆斯特丹大学）；Ahold Delhaize Research（阿霍德德尔海兹研究院）
