@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://aclanthology.org/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"
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<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</abstract>
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%0 Conference Proceedings
%T JaCorpTrack: Corporate History Event Extraction for Tracking Organizational Changes
%A Sawada, Yuya
%A Ouchi, Hiroki
%A Yasui, Yuichiro
%A Teranishi, Hiroki
%A Matsumoto, Yuji
%A Watanabe, Taro
%A Ishii, Masayuki
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F sawada-etal-2025-jacorptrack
%X 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
%U https://aclanthology.org/2025.emnlp-industry.177/
%P 2607-2623
Markdown (Informal)
[JaCorpTrack: Corporate History Event Extraction for Tracking Organizational Changes](https://aclanthology.org/2025.emnlp-industry.177/) (Sawada et al., EMNLP 2025)
ACL