{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:54Z","timestamp":1761176274662,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"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":[[2025,10,21]]},"abstract":"<jats:p>Identifying entities in medical text often involves dealing with discontinuous word sequences or entities sharing a common head, which pose significant challenges for traditional Named Entity Recognition (NER) systems. Current state-of-the-art discontinuous NER models typically process each sentence in isolation, overlooking valuable intra-sentence context. However, recent studies have shown that large language models (LLMs) perform exceptionally well when provided such context. In this work, we introduce DocDiscNER, a novel approach to discontinuous NER, which features (i) a context-aware document chunking method that provides contextually related segments as input for LLM-based NER models; (ii) a dataset and approach for coordination ellipses resolution, to address candidate spans sharing common heads and (iii) a self-consistency decoding strategy that uses self-ensembling and a majority voting mechanism to select the most consistent predictions as entity spans. We demonstrate the effectiveness and generalisability of our method on three discontinuous NER benchmarks, achieving new state-of-the-art (SOTA) performance on two of them\u2013CADEC and ShARe-14 (2.48 and 2.2 absolute F1 points gain, respectively); while achieving competitive results on ShARe-13. In addition, our method surpasses previous SOTA performance specifically in recognising discontinuous mentions. A deeper analysis unveils that incorporating semantically relevant context significantly enhances overall NER performance compared to using individual sentences as input.<\/jats:p>","DOI":"10.3233\/faia251338","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:58:37Z","timestamp":1761127117000},"source":"Crossref","is-referenced-by-count":0,"title":["DocDiscNER: Enhanced Document-Level Discontinuous NER via Coordination Ellipses Resolution and Self-Consistency Decoding"],"prefix":"10.3233","author":[{"given":"Areej","family":"Alhassan","sequence":"first","affiliation":[{"name":"The University of Manchester, Manchester, United Kingdom"},{"name":"King Saud University, Riyadh, Saudi Arabia"}]},{"given":"Viktor","family":"Schlegel","sequence":"additional","affiliation":[{"name":"The University of Manchester, Manchester, United Kingdom"},{"name":"Imperial College London, Imperial Global Singapore"}]},{"given":"Rina Carines","family":"Cabral","sequence":"additional","affiliation":[{"name":"The University of Sydney, Sydney, Australia"}]},{"given":"Riza","family":"Batista-Navarro","sequence":"additional","affiliation":[{"name":"The University of Manchester, Manchester, United Kingdom"}]},{"given":"Soyeon Caren","family":"Han","sequence":"additional","affiliation":[{"name":"The University of Melbourne, Melbourne, Australia"}]},{"given":"Josiah","family":"Poon","sequence":"additional","affiliation":[{"name":"The University of Sydney, Sydney, Australia"}]},{"given":"Goran","family":"Nenadic","sequence":"additional","affiliation":[{"name":"The University of Manchester, Manchester, United Kingdom"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251338","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:58:37Z","timestamp":1761127117000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251338"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251338","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}