{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:32:04Z","timestamp":1773246724141,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683645","type":"print"},{"value":"9781643683652","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"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":[[2022,12,5]]},"abstract":"<jats:p>In this paper, we explore legal argument mining using multiple levels of granularity. Argument mining has usually been conceptualized as a sentence classification problem. In this work, we conceptualize argument mining as a token-level (i.e., word-level) classification problem. We use a Longformer model to classify the tokens. Results show that token-level text classification identifies certain legal argument elements more accurately than sentence-level text classification. Token-level classification also provides greater flexibility to analyze legal texts and to gain more insight into what the model focuses on when processing a large amount of input data.<\/jats:p>","DOI":"10.3233\/faia220477","type":"book-chapter","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T11:51:47Z","timestamp":1670586707000},"source":"Crossref","is-referenced-by-count":5,"title":["Multi-Granularity Argument Mining in Legal Texts"],"prefix":"10.3233","author":[{"given":"Huihui","family":"Xu","sequence":"first","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh"},{"name":"Learning Research and Development Center, University of Pittsburgh"}]},{"given":"Kevin","family":"Ashley","sequence":"additional","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh"},{"name":"Learning Research and Development Center, University of Pittsburgh"},{"name":"School of Law, University of Pittsburgh"}]}],"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\/FAIA220477","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T11:51:48Z","timestamp":1670586708000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220477"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"ISBN":["9781643683645","9781643683652"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220477","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,5]]}}}