{"id":"https://openalex.org/W7166809116","doi":"https://doi.org/10.18653/v1/2026.bionlp-2.21","title":"CASPAR: A Context-Aware Span Refinement Approach for Decision Support","display_name":"CASPAR: A Context-Aware Span Refinement Approach for Decision Support","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166809116","doi":"https://doi.org/10.18653/v1/2026.bionlp-2.21"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.bionlp-2.21","doi":"https://doi.org/10.18653/v1/2026.bionlp-2.21","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.bionlp-2.21","pdf_url":"https://aclanthology.org/2026.bionlp-2.21.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the BioNLP 2026 (Shared Tasks)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.bionlp-2.21.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139741463","display_name":"Jing Tao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I204722609","display_name":"Queen's University","ror":"https://ror.org/02y72wh86","country_code":"CA","type":"education","lineage":["https://openalex.org/I204722609"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jing Tao","raw_affiliation_strings":["School of Computing Queen's University Kingston , ON , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing Queen's University Kingston , ON , Canada","institution_ids":["https://openalex.org/I204722609"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106602660","display_name":"Amir Eskandari","orcid":"https://orcid.org/0009-0002-0760-4715","observed_orcids":["https://orcid.org/0009-0002-0760-4715"]},"institutions":[{"id":"https://openalex.org/I204722609","display_name":"Queen's University","ror":"https://ror.org/02y72wh86","country_code":"CA","type":"education","lineage":["https://openalex.org/I204722609"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Amir Eskandari","raw_affiliation_strings":["School of Computing Queen's University Kingston , ON , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing Queen's University Kingston , ON , Canada","institution_ids":["https://openalex.org/I204722609"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063480277","display_name":"Farhana H. Zulkernine","orcid":"https://orcid.org/0000-0002-3326-0875","observed_orcids":["https://orcid.org/0000-0002-3326-0875"]},"institutions":[{"id":"https://openalex.org/I204722609","display_name":"Queen's University","ror":"https://ror.org/02y72wh86","country_code":"CA","type":"education","lineage":["https://openalex.org/I204722609"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Farhana Zulkernine","raw_affiliation_strings":["School of Computing Queen's University Kingston , ON , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing Queen's University Kingston , ON , Canada","institution_ids":["https://openalex.org/I204722609"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204722609"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40122415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"146","last_page":"154"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.8237000107765198,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.8237000107765198,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.14180000126361847,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.012799999676644802,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/decision-support","display_name":"decision support","score":0.5180000066757202},{"id":"https://openalex.org/keywords/clinical-narratives","display_name":"clinical text","score":0.6439999938011169},{"id":"https://openalex.org/keywords/sequence-labeling","display_name":"sequence labeling","score":0.5299999713897705},{"id":"https://openalex.org/keywords/bigru","display_name":"BiGRU","score":0.7649999856948853},{"id":"https://openalex.org/keywords/demographic-subgroups","display_name":"demographic subgroups","score":0.6330000162124634}],"concepts":[{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.6087999939918518},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5509999990463257},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3467999994754791},{"id":"https://openalex.org/C2778753569","wikidata":"https://www.wikidata.org/wiki/Q1960395","display_name":"Span (engineering)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C186116695","wikidata":"https://www.wikidata.org/wiki/Q5249226","display_name":"Decision analysis","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31150001287460327},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C56397880","wikidata":"https://www.wikidata.org/wiki/Q6044094","display_name":"Intelligent decision support system","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C2984634286","wikidata":"https://www.wikidata.org/wiki/Q1331926","display_name":"Decision process","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.bionlp-2.21","doi":"https://doi.org/10.18653/v1/2026.bionlp-2.21","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.bionlp-2.21","pdf_url":"https://aclanthology.org/2026.bionlp-2.21.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the BioNLP 2026 (Shared Tasks)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.bionlp-2.21","doi":"https://doi.org/10.18653/v1/2026.bionlp-2.21","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.bionlp-2.21","pdf_url":"https://aclanthology.org/2026.bionlp-2.21.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the BioNLP 2026 (Shared Tasks)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166809116.pdf","grobid_xml":"https://content.openalex.org/works/W7166809116.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7166814604","https://openalex.org/W7166812821","https://openalex.org/W7166885247","https://openalex.org/W7166804782","https://openalex.org/W7140109144","https://openalex.org/W4402683049","https://openalex.org/W7166854268","https://openalex.org/W7166874944","https://openalex.org/W2941310691","https://openalex.org/W3173264542"],"abstract_inverted_index":{"Extracting":[0],"structured":[1],"clinical":[2],"information":[3],"by":[4],"selecting":[5],"a":[6,68,93,114,118,120,127,134,154],"text":[7,37,60,66,100,176],"span":[8,38,61,101,136,143,177],"from":[9,64,81],"long":[10],"medical":[11,57],"discharge":[12,77],"summaries":[13,78],"using":[14],"Natural":[15],"Language":[16],"Processing":[17],"(NLP)":[18],"poses":[19],"difficult":[20],"challenges.Heterogeneity":[21],"of":[22,27,36,117,157],"the":[23,28,47,56,82,99,162],"text,":[24],"semantic":[25],"overlap":[26],"decision":[29,74],"categories,":[30],"and":[31,59,103,126,149,179],"uncertainty":[32],"in":[33,50,175],"identifying":[34],"boundaries":[35],"aligned":[39],"with":[40,113],"highly":[41],"unbalanced":[42],"labels.Contribute":[43],"to":[44,144],"these":[45],"challenges,":[46],"MedExACT":[48],"competition":[49],"BioNLP":[51],"@":[52],"ACL":[53],"2026":[54],"formulates":[55],"concept":[58],"extraction":[62,102,178],"problem":[63],"unstructured":[65],"as":[67],"classification":[69,104],"task":[70,105],"over":[71],"nine":[72],"DICTUM":[73],"categories.Unstructured":[75],"ICU":[76],"are":[79],"used":[80],"MedDec":[83],"dataset,":[84],"for":[85,98],"solution":[86],"approach":[87,97],"across":[88],"eight":[89],"demographic":[90],"subgroups.We":[91],"propose":[92],"two-stage":[94],"data":[95],"processing":[96],"where":[106],"Stage":[107],"1":[108],"performs":[109],"document-level":[110],"sequence":[111],"labeling":[112],"pipeline":[115],"composed":[116],"RoBERTabase,":[119],"Bidirectional":[121],"Gated":[122],"Recurrent":[123],"Unit":[124],"(Bi-GRU),":[125],"Conditional":[128],"Random":[129],"Field":[130],"(CRF).Stage":[131],"2":[132],"applies":[133],"local":[135],"refinement":[137],"module":[138],"which":[139,159],"revisits":[140],"each":[141],"candidate":[142],"adjust":[145],"both":[146],"category":[147],"assignments":[148],"boundary":[150],"offsets.Our":[151],"framework":[152],"achieves":[153],"final":[155],"score":[156],"0.5668":[158],"substantially":[160],"outperforms":[161],"provided":[163],"baseline.Ablation":[164],"results":[165],"further":[166],"suggest":[167],"that":[168],"BiGRU-based":[169],"contextual":[170],"consolidation":[171],"improves":[172],"CRF-based":[173],"decoding":[174],"classification.":[180]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-07-02T00:00:00"}