{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:10:24Z","timestamp":1764850224558,"version":"3.46.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686387","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>Argument Mining (AM) [1], the task of extracting arguments and their relations (e.g. attacks, supports) from text, has lead to potential applications of formal models of argumentation on real-world scenarios. However, AM methods largely depend on Machine Learning (ML) techniques, which require high-quality annotated data. This poses a significant limitation in domains like legal reasoning, where annotated corpora are extremely scarce. Current legal AM research relies almost entirely on a single dataset from the European Court of Human Rights (ECHR) [2], limiting the development of robust, argumentation-driven automated legal reasoning systems. Addressing this data scarcity is essential for advancing computational legal argumentation. To address this challenge, this paper briefly describes some preliminary results that demonstrate the promising potential of LLMs to assist legal experts in annotating additional legal corpora. This advancement paves the way for more robust, argumentation-driven automated reasoning systems in the legal domain. More details on our approach and experimental results are provided in [3].<\/jats:p>","DOI":"10.3233\/faia251615","type":"book-chapter","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:05:39Z","timestamp":1764849939000},"source":"Crossref","is-referenced-by-count":0,"title":["Discovering the Potential of LLMs in Annotating Legal Texts for Argument Mining (Extended Abstract)"],"prefix":"10.3233","author":[{"given":"Christina","family":"Berghegger","sequence":"first","affiliation":[{"name":"IRIT, Universit\u00e9\u00a0Toulouse Capitole"}]},{"given":"C\u00e9sar","family":"Philippe","sequence":"additional","affiliation":[{"name":"IRIT, Universit\u00e9\u00a0Toulouse Capitole"}]},{"given":"Karla","family":"Salas-Jimenez","sequence":"additional","affiliation":[{"name":"IRIT, Universit\u00e9\u00a0Toulouse Capitole"}]},{"given":"Jean-Guy","family":"Mailly","sequence":"additional","affiliation":[{"name":"IRIT, Universit\u00e9\u00a0Toulouse Capitole"}]},{"given":"Leila","family":"Moudjari","sequence":"additional","affiliation":[{"name":"IRIT, Universit\u00e9\u00a0Toulouse Capitole"}]},{"given":"Laurent","family":"Perrussel","sequence":"additional","affiliation":[{"name":"IRIT, Universit\u00e9\u00a0Toulouse Capitole"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Legal Knowledge and Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:05:40Z","timestamp":1764849940000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"ISBN":["9781643686387"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251615","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]}}}