ANNOTARES: A Dataset for Extracting Logical Structures from German Statutory Texts

2026-08-04

AI Summary

This study addresses the challenging task of automatically identifying and segmenting legal conditions (Tatbestand) from legal consequences (Rechtsfolge) in German statutory texts. To facilitate research on this structural parsing problem, the authors introduce ANNOTARES, the first fine-grained annotated dataset covering three major German legal codes, enabling cross-code generalization studies. The work systematically evaluates a range of approaches, including rule-based baselines, CRF, BiLSTM, BiLSTM-CRF, and Transformer architectures based on BERT and large language models. Experimental results demonstrate that BERT-based and large language models significantly outperform traditional methods in capturing the complex syntactic structures inherent in legal texts, thereby confirming the effectiveness of pretrained language models for extracting logical structures in legal documents.

Abstract

The automatic structural analysis of legal texts is a cornerstone of legal technology, yet the extraction of their logical components remains a significant challenge. In this paper, we introduce the task of identifying and segmenting legal conditions (Tatbestand) and legal consequences (Rechtsfolge) within German statutory texts. To support this task, we present ANNOTARES (Annotations of Tatbestand-Rechtsfolge Sequences), a novel dataset comprising German law texts with span-level annotations. Spanning three distinct legal codes, the dataset is designed to evaluate both domain-specific performance and cross-statute generalizability. We benchmark diverse architectural approaches: a rule-based baseline, CRFs, BiLSTMs, BiLSTM-CRF, and modern Transformer-based models, including BERT variants and LLM-based methods. Our results demonstrate that BERT and LLM-based models achieve superior performance in capturing the complex syntactic structures of legal language. We release our dataset to facilitate further research in automated legal reasoning.

Problem

• legal text
• logical structure
• Tatbestand
• Rechtsfolge
• statutory text

Innovation

• legal text segmentation
• Tatbestand-Rechtsfolge extraction
• span-level annotation
• legal NLP
• Transformer-based models