@inproceedings{sawada-etal-2025-jacorptrack,
    title = "{J}a{C}orp{T}rack: Corporate History Event Extraction for Tracking Organizational Changes",
    author = "Sawada, Yuya  and
      Ouchi, Hiroki  and
      Yasui, Yuichiro  and
      Teranishi, Hiroki  and
      Matsumoto, Yuji  and
      Watanabe, Taro  and
      Ishii, Masayuki",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-industry.177/",
    doi = "10.18653/v1/2025.emnlp-industry.177",
    pages = "2607--2623",
    ISBN = "979-8-89176-333-3",
    abstract = "Corporate history in corporate annual reports includes events related to organizational changes, which can provide useful cues for a comprehensive understanding of corporate actions.However, extracting organizational changes requires identifying differences in companies before and after an event, raising concerns about whether existing information extraction systems can accurately capture the relations.This work introduces JaCorpTrack, a novel event extraction task designed to identify events related to organizational changes.JaCorpTrack defines five event types related to organizational changes and is designed to identify the company names before and after each event, as well as the corresponding date.Experimental results indicate that large language models (LLMs) exhibit notable disparities in performance across event types.Our analysis reveals that these systems face challenges in identifying company names before and after events, and in interpreting event types expressed under ambiguous terminology.We will publicly release our dataset and experimental code at https://github.com/naist-nlp/JaCorpTrack"
}