Multiple Choice Question Answering in the Legal Domain Using Reinforced Co-occurrence

Dataset of the paper: J. Martinez-Gil, Bernhard Freudenthaler, A Min Tjoa: Multiple Choice Question Answering in the Legal Domain Using Reinforced Co-occurrence. DEXA (1) 2019: 138-148

📖 Introduction

This dataset is a comprehensive collection of multiple-choice questions (MCQs) focused on the legal domain. It covers a broad range of topics such as European Commission procedures, legal document types, court system processes, legal definitions, and laws of the European Union and United Kingdom.

Citation

If you find this dataset useful, please cite the original work:

@inproceedings{GilFT19,
  title     = {Multiple Choice Question Answering in the Legal Domain Using Reinforced Co-occurrence},
  author    = {Jorge Martinez-Gil and Bernhard Freudenthaler and A Min Tjoa},
  booktitle = {Database and Expert Systems Applications - 30th International Conference, {DEXA} 2019, Linz, Austria, August 26-29, 2019, Proceedings, Part {I}},
  year      = {2019},
  publisher = {Springer},
  pages     = {138--148},
  series    = {Lecture Notes in Computer Science},
  volume    = {11706},
  doi       = {10.1007/978-3-030-27615-7_10},
  url       = {https://doi.org/10.1007/978-3-030-27615-7_10}
}

📊 Dataset Structure

The dataset is formatted as a JSON array, with each element representing a distinct multiple-choice question (MCQ). The structure of each question object is outlined below:

Key - Description - Type

• id - Unique identifier for the question - Integer
• question - The question text - String
• options - An array of answer options - Array of objects

Example JSON Object

{
  "id": 1,
  "question": "What type of form must a claimant complete to initiate legal action in a civil court?",
  "options": [
    {"option": "Civil action", "is_correct": false},
    {"option": "Track form", "is_correct": false},
    {"option": "Claim form", "is_correct": true},
    {"option": "Business form", "is_correct": false}
  ]
}

🚀 Usage

This dataset can be employed for various purposes, including:

• MCQA Accuracy Assessment: Evaluate the precision of multiple-choice question answering systems in the legal domain.
• Machine Learning Training: Use this dataset to train AI models focused on assessing legal knowledge.

📜 License

This dataset is available for educational use. For commercial applications, please adhere to applicable copyright and licensing regulations.

🙏 Acknowledgments

This dataset is intended for educational and informational purposes and should not be considered a substitute for professional legal advice. Users should consult with legal experts for specific legal issues.

⚠️ Note

Ensure the verification of the legal information's accuracy and relevance before using it, as legal standards and regulations may evolve over time.