@inproceedings{mali-etal-2024-information,
    title = "Information Extraction for Planning Court Cases",
    author = "Mali, Drish  and
      Mali, Rubash  and
      Barale, Claire",
    editor = "Aletras, Nikolaos  and
      Chalkidis, Ilias  and
      Barrett, Leslie  and
      Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina  and
      Preoțiuc-Pietro, Daniel  and
      Spanakis, Gerasimos",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024",
    month = nov,
    year = "2024",
    address = "Miami, FL, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.nllp-1.8/",
    doi = "10.18653/v1/2024.nllp-1.8",
    pages = "97--114",
    abstract = "Legal documents are often long and unstructured, making them challenging and time-consuming to apprehend. An automatic system that can identify relevant entities and labels within legal documents, would significantly reduce the legal research time. We developed a system to streamline legal case analysis from planning courts by extracting key information from XML files using Named Entity Recognition (NER) and multi-label classification models to convert them into structured form. This research contributes three novel datasets for the Planning Court cases: a NER dataset, a multi-label dataset fully annotated by humans, and newly re-annotated multi-label datasets partially annotated using LLMs. We experimented with various general-purpose and legal domain-specific models with different maximum sequence lengths. It was noted that incorporating paragraph position information improved the performance of models for the multi-label classification task. Our research highlighted the importance of domain-specific models, with LegalRoBERTa and LexLM demonstrating the best performance."
}