{"id":"https://openalex.org/W7126054161","doi":"https://doi.org/10.1109/bibm66473.2025.11356248","title":"EICL: Entity-Aware In-Context Learning for BioNER via Hybrid Semantic-Terminological Retrieval","display_name":"EICL: Entity-Aware In-Context Learning for BioNER via Hybrid Semantic-Terminological Retrieval","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126054161","doi":"https://doi.org/10.1109/bibm66473.2025.11356248"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019391056","display_name":"Z. Liu","orcid":"https://orcid.org/0009-0003-3826-6437"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhengze Liu","raw_affiliation_strings":["School of Computer Science and Technology, Dalian University of Technology,Dalian,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Dalian University of Technology,Dalian,China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124241989","display_name":"Nianmin Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nianmin Yao","raw_affiliation_strings":["School of Computer Science and Technology, Dalian University of Technology,Dalian,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Dalian University of Technology,Dalian,China","institution_ids":["https://openalex.org/I27357992"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019391056"],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.67320703,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.6171000003814697,"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":0.6171000003814697,"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/T10028","display_name":"Topic Modeling","score":0.2847000062465668,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.04879999905824661,"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/terminology","display_name":"Terminology","score":0.5960000157356262},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5648999810218811},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.47780001163482666},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.43160000443458557},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.41449999809265137},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4050000011920929},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.36730000376701355},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.34049999713897705}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.826200008392334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6360999941825867},{"id":"https://openalex.org/C547195049","wikidata":"https://www.wikidata.org/wiki/Q1725664","display_name":"Terminology","level":2,"score":0.5960000157356262},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5648999810218811},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.47780001163482666},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44029998779296875},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39820000529289246},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39800000190734863},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.36730000376701355},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.32089999318122864},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27639999985694885},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.2696000039577484},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6713122129440308,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2149369282","https://openalex.org/W2163107094","https://openalex.org/W2169099542","https://openalex.org/W2613053618","https://openalex.org/W3133665323","https://openalex.org/W4252076394","https://openalex.org/W4389524546","https://openalex.org/W4393094733"],"related_works":[],"abstract_inverted_index":{"In-Context":[0,68],"Learning":[1,69],"(ICL)":[2],"has":[3],"become":[4],"an":[5,88],"effective":[6],"paradigm":[7],"for":[8,58,101,137,163],"biomedical":[9,164],"named":[10],"entity":[11,52,99],"recognition":[12],"(BioNER),":[13],"allowing":[14],"large":[15],"language":[16],"models":[17],"to":[18,96],"recognize":[19],"entities":[20],"using":[21],"only":[22],"a":[23,71,107,125],"few":[24],"examples":[25],"without":[26],"extensive":[27],"fine-tuning.":[28],"However,":[29],"ICL's":[30],"effectiveness":[31,162],"depends":[32],"heavily":[33],"on":[34,44],"selecting":[35],"appropriate":[36],"examples,":[37],"and":[38,54,82,160],"current":[39],"approaches":[40],"that":[41,73,92,113,129,149],"rely":[42],"primarily":[43],"sentence-level":[45],"semantic":[46,131],"similarity":[47,132],"often":[48],"miss":[49],"the":[50],"fine-grained":[51],"distinctions":[53],"domain-specific":[55],"characteristics":[56],"crucial":[57],"BioNER":[59,145],"tasks.":[60],"To":[61],"address":[62],"these":[63],"limitations,":[64],"we":[65],"propose":[66],"Entity-aware":[67],"(EICL),":[70],"framework":[72],"improves":[74],"example":[75,140,154],"selection":[76,155],"by":[77],"incorporating":[78],"both":[79],"entity-centric":[80],"information":[81],"domainspecific":[83],"terminology":[84,134],"overlap.":[85],"EICL":[86,150],"employs":[87],"Entity":[89],"Expansion":[90],"Module":[91,112],"uses":[93],"LLM":[94],"capabilities":[95],"create":[97],"explicit":[98],"representations":[100],"better":[102],"structural":[103],"alignment,":[104],"along":[105],"with":[106,133],"Domain":[108],"Vocabulary":[109],"Overlap":[110],"Retrieval":[111],"measures":[114],"domain":[115],"compatibility":[116],"through":[117],"terminological":[118],"analysis.":[119],"These":[120],"components":[121],"are":[122],"integrated":[123],"within":[124],"hybrid":[126],"retrieval":[127],"strategy":[128],"combines":[130],"overlap":[135],"scores":[136],"more":[138],"accurate":[139],"selection.":[141],"Experiments":[142],"across":[143],"multiple":[144],"benchmark":[146],"datasets":[147],"show":[148],"outperforms":[151],"existing":[152],"ICL":[153],"methods,":[156],"demonstrating":[157],"improved":[158],"generalization":[159],"practical":[161],"applications.":[165]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}