用BERT模型实现塞尔维亚语法律文书命名实体识别，提升法律文本自动化处理效率。
Named entity recognition for Serbian legal documents: Design, methodology and dataset development
- 基于BERT的双向编码器设计，适配塞尔维亚语法律文本特征。
- 在人工标注数据集上达均值F1分数0.96，验证系统有效性。
- 适合法律科技、司法自动化领域研究者与从业者参考。
自然语言处理（NLP）领域近年来快速发展，尤其是大语言模型（LLMs）的应用推动了各类文档处理工具的设计与文档归档、搜索和检索流程的优化。官方法律文书因其每日生成海量数据且涉及律师、法务机构、行政人员及公众等广泛群体，成为极具研究价值的领域。本文提出一种基于大语言模型的塞尔维亚语法律文书命名实体识别（NER）解决方案，利用预训练的双向编码器表示（BERT）进行针对性微调，以识别并分类文本中的关键信息。研究构建了首个面向塞尔维亚语的公开法院判决书标注数据集，并系统阐述了模型设计与方法论。交叉验证结果显示，该模型在自建数据集上达到均值F1分数0.96，对故意修改输入文本的测试也表现出良好鲁棒性，证明了该方案的有效性与实用性。
原文摘要 · Abstract (English)
Recent advancements in the field of natural language processing (NLP) and especially large language models (LLMs) and their numerous applications have brought research attention to design of different document processing tools and enhancements in the process of document archiving, search and retrieval. Domain of official, legal documents is especially interesting due to vast amount of data generated on the daily basis, as well as the significant community of interested practitioners (lawyers, law offices, administrative workers, state institutions and citizens). Providing efficient ways for automation of everyday work involving legal documents is therefore expected to have significant impact in different fields. In this work we present one LLM based solution for Named Entity Recognition (NER) in the case of legal documents written in Serbian language. It leverages on the pre-trained bidirectional encoder representations from transformers (BERT), which had been carefully adapted to the specific task of identifying and classifying specific data points from textual content. Besides novel dataset development for Serbian language (involving public court rulings), presented system design and applied methodology, the paper also discusses achieved performance metrics and their implications for objective assessment of the proposed solution. Performed cross-validation tests on the created manually labeled dataset with mean $F_1$ score of 0.96 and additional results on the examples of intentionally modified text inputs confirm applicability of the proposed system design and robustness of the developed NER solution.
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