@inproceedings{quinta-de-castro-da-silva-2025-labor,
    title = "Labor Lex: A New {P}ortuguese Corpus and Pipeline for Information Extraction in {B}razilian Legal Texts",
    author = "Quinta de Castro, Pedro Vitor  and
      Da Silva, N{\'a}dia F{\'e}lix Felipe",
    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 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.nllp-1.14/",
    doi = "10.18653/v1/2025.nllp-1.14",
    pages = "184--206",
    ISBN = "979-8-89176-338-8",
    abstract = "Relation Extraction (RE) is a challenging Natural Language Processing task that involves identifying named entities from text and classifying the relationships between them. When applied to a specific domain, the task acquires a new layer of complexity, handling the lexicon and context particular to the domain in question. In this work, this task is applied to the Legal domain, specifically targeting Brazilian Labor Law. Architectures based on Deep Learning, with word representations derived from Transformer Language Models (LM), have shown state-of-the-art performance for the RE task. Recent works on this task handle Named Entity Recognition (NER) and RE either as a single joint model or as a pipelined approach. In this work, we introduce Labor Lex, a newly constructed corpus based on public documents from Brazilian Labor Courts. We also present a pipeline of models trained on it. Different experiments are conducted for each task, comparing supervised training using LMs and In-Context Learning (ICL) with Large Language Models (LLM), and verifying and analyzing the results for each one. For the NER task, the best achieved result was 89.97{\%} F1-Score, and for the RE task, the best result was 82.38{\%} F1-Score. The best results for both tasks were obtained using the supervised training approach."
}