🤖 AI Summary
To address the scarcity of high-quality resources and domain-adapted models for Named Entity Recognition (NER) in Serbian legal texts (e.g., court judgments), this paper introduces the first expert-annotated, domain-specific NER dataset for Serbian legal language. We propose a BERT-based domain-adaptive fine-tuning approach: leveraging continued pretraining on legal corpora to enhance domain specificity, employing a bidirectional encoder architecture, and rigorously evaluating performance via stratified cross-validation. Experimental results show that our model achieves an F1 score of 0.96 on a held-out test set—substantially outperforming off-the-shelf general-purpose models—and demonstrates strong robustness against lexical perturbations and formatting variations. Our key contributions are: (1) the first open-source, expert-annotated NER benchmark dataset for Serbian legal texts; and (2) a reproducible, high-accuracy, and robust end-to-end NER solution tailored to the Serbian legal domain.
📝 Abstract
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.