{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T06:49:53Z","timestamp":1768718993631,"version":"3.49.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020R1A2C3010638"],"award-info":[{"award-number":["NRF-2020R1A2C3010638"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2014M3C9A3063541"],"award-info":[{"award-number":["NRF-2014M3C9A3063541"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea Health Technology R&D Project","award":["HR20C0021"],"award-info":[{"award-number":["HR20C0021"]}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Health & Welfare, Republic of Korea; and the Research Collaboration Project from AstraZeneca UK"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Current studies in extractive question answering (EQA) have modeled the single-span extraction setting, where a single answer span is a label to predict for a given question-passage pair. This setting is natural for general domain EQA as the majority of the questions in the general domain can be answered with a single span. Following general domain EQA models, current biomedical EQA (BioEQA) models utilize the single-span extraction setting with post-processing steps.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this article, we investigate the question distribution across the general and biomedical domains and discover biomedical questions are more likely to require list-type answers (multiple answers) than factoid-type answers (single answer). This necessitates the models capable of producing multiple answers for a question. Based on this preliminary study, we propose a sequence tagging approach for BioEQA, which is a multi-span extraction setting. Our approach directly tackles questions with a variable number of phrases as their answer and can learn to decide the number of answers for a question from training data. Our experimental results on the BioASQ 7b and 8b list-type questions outperformed the best-performing existing models without requiring post-processing steps.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Source codes and resources are freely available for download at https:\/\/github.com\/dmis-lab\/SeqTagQA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac397","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T13:38:21Z","timestamp":1655473101000},"page":"3794-3801","source":"Crossref","is-referenced-by-count":18,"title":["Sequence tagging for biomedical extractive question answering"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6435-548X","authenticated-orcid":false,"given":"Wonjin","family":"Yoon","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University , Seoul 02841, South Korea"}]},{"given":"Richard","family":"Jackson","sequence":"additional","affiliation":[{"name":"AstraZeneca UK , Cambridge CB2 0AA, UK"}]},{"given":"Aron","family":"Lagerberg","sequence":"additional","affiliation":[{"name":"AstraZeneca SE , 43150 M\u00f6lndal, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6798-9106","authenticated-orcid":false,"given":"Jaewoo","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University , Seoul 02841, South Korea"},{"name":"AIGEN Sciences Inc. , Seoul 04778, South Korea"}]}],"member":"286","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"2023041405345408600_","first-page":"2924","author":"Clark","year":"2019"},{"key":"2023041405345408600_","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1542\/peds.113.1.64","article-title":"An evaluation of information-seeking behaviors of general pediatricians","volume":"113","author":"D'Alessandro","year":"2004","journal-title":"Pediatrics"},{"key":"2023041405345408600_","first-page":"4171","author":"Devlin","year":"2019"},{"key":"2023041405345408600_","first-page":"382","article-title":"Lifelong self-directed learning using a computer database of clinical questions","volume":"45","author":"Ely","year":"1997","journal-title":"J. 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