{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:14:14Z","timestamp":1771697654770,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682525","type":"print"},{"value":"9781643682532","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T00:00:00Z","timestamp":1638403200000},"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":[[2021,12,2]]},"abstract":"<jats:p>In this paper, we treat sentence annotation as a classification task. We employ sequence-to-sequence models to take sentence position information into account in identifying case law sentences as issues, conclusions, or reasons. We also compare the legal domain specific sentence embedding with other general purpose sentence embeddings to gauge the effect of legal domain knowledge, captured during pre-training, on text classification. We deployed the models on both summaries and full-text decisions. We found that the sentence position information is especially useful for full-text sentence classification. We also verified that legal domain specific sentence embeddings perform better, and that meta-sentence embedding can further enhance performance when sentence position information is included.<\/jats:p>","DOI":"10.3233\/faia210314","type":"book-chapter","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T09:47:28Z","timestamp":1638870448000},"source":"Crossref","is-referenced-by-count":2,"title":["Accounting for Sentence Position and Legal Domain Sentence Embedding in Learning to Classify Case Sentences"],"prefix":"10.3233","author":[{"given":"Huihui","family":"Xu","sequence":"first","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh"}]},{"given":"Jaromir","family":"Savelka","sequence":"additional","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University"}]},{"given":"Kevin D.","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\/FAIA210314","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T09:47:29Z","timestamp":1638870449000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,2]]},"ISBN":["9781643682525","9781643682532"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210314","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,2]]}}}