Sequence tagging for biomedical extractive question answering
DC Field | Value | Language |
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dc.contributor.author | Yoon, Wonjin | - |
dc.contributor.author | Jackson, Richard | - |
dc.contributor.author | Lagerberg, Aron | - |
dc.contributor.author | Kang, Jaewoo | - |
dc.date.accessioned | 2022-12-09T16:00:09Z | - |
dc.date.available | 2022-12-09T16:00:09Z | - |
dc.date.created | 2022-12-08 | - |
dc.date.issued | 2022-08-02 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/146612 | - |
dc.description.abstract | Motivation: 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. Results: 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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.title | Sequence tagging for biomedical extractive question answering | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kang, Jaewoo | - |
dc.identifier.doi | 10.1093/bioinformatics/btac397 | - |
dc.identifier.scopusid | 2-s2.0-85135707246 | - |
dc.identifier.wosid | 000819025100001 | - |
dc.identifier.bibliographicCitation | BIOINFORMATICS, v.38, no.15, pp.3794 - 3801 | - |
dc.relation.isPartOf | BIOINFORMATICS | - |
dc.citation.title | BIOINFORMATICS | - |
dc.citation.volume | 38 | - |
dc.citation.number | 15 | - |
dc.citation.startPage | 3794 | - |
dc.citation.endPage | 3801 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
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