README.md

metadata

license: apache-2.0
dataset_info:
  features:
    - name: premise
      dtype: string
    - name: hypothesis
      dtype: string
    - name: label
      dtype: string
  splits:
    - name: train
      num_bytes: 1858442640
      num_examples: 474283
    - name: validation
      num_bytes: 18996841
      num_examples: 5000
    - name: test
      num_bytes: 19683829
      num_examples: 5000
  download_size: 725637794
  dataset_size: 1897123310
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
task_categories:
  - sentence-similarity
language:
  - tr
tags:
  - legal
size_categories:
  - 100K<n<1M

Turkish Law NLI Dataset

This dataset is derived from case files of Turkish Commercial Courts and was prepared as part of a student project to contribute to the Turkish NLP literature.

Source Data

The dataset was created by collecting approximately 33,000 case rulings from open sources using web scraping methods. The dataset includes only the "summary" sections of the case rulings, where the reason for each lawsuit is typically described.

Data Structure and Labeling

• The dataset was adapted for sentence similarity tasks, inspired by the SNLI dataset. The goal of this project is to develop a semantic search model for identifying relevant precedent cases in legal settings.
• This is the first version of the dataset, and future versions will incorporate additional metadata and employ more refined labeling techniques.

Some sections of the Tree Structure

Labeling Methodology

To establish relationships between case files, legal articles within each case were utilized. Only commercial cases governed by the Turkish Commercial Code (TTK) are included. Articles from the TTK were aligned in a hierarchical structure, considering main and subheadings, and were transformed into a tree structure. The relationship between cases was determined by calculating distances between the articles they contain within this tree structure.

Label Types

• Entailment: For each case, the 7 closest cases (with lower distances indicating closer relationships) were labeled as related.
• Contradiction: For each case, the 7 most distant cases were labeled as unrelated.
• Neutral: Each case was labeled as neutral with respect to the legal articles it contains.

Contributors

• Mesut Demirel
• Recep Karabulut