{"id":"https://openalex.org/W4417113876","doi":"https://doi.org/10.48550/arxiv.2510.08588","title":"Enhancing Biomedical Named Entity Recognition using GLiNER-BioMed with Targeted Dictionary-Based Post-processing for BioASQ 2025 task 6","display_name":"Enhancing Biomedical Named Entity Recognition using GLiNER-BioMed with Targeted Dictionary-Based Post-processing for BioASQ 2025 task 6","publication_year":2025,"publication_date":"2025-10-03","ids":{"openalex":"https://openalex.org/W4417113876","doi":"https://doi.org/10.48550/arxiv.2510.08588"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.08588","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08588","pdf_url":"https://arxiv.org/pdf/2510.08588","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.08588","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047331275","display_name":"Ritesh Mehta","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mehta, Ritesh","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5047331275"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.7265999913215637,"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":0.7265999913215637,"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":0.18289999663829803,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.010599999688565731,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.817300021648407},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7897999882698059},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.666700005531311},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.6643999814987183},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5108000040054321},{"id":"https://openalex.org/keywords/entity-linking","display_name":"Entity linking","score":0.4699000120162964},{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.4498000144958496},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.44620001316070557}],"concepts":[{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.817300021648407},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7897999882698059},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372000217437744},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.666700005531311},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.6643999814987183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6247000098228455},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5403000116348267},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5108000040054321},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.4699000120162964},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.4498000144958496},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42489999532699585},{"id":"https://openalex.org/C2777889803","wikidata":"https://www.wikidata.org/wiki/Q25047676","display_name":"Named entity","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.36250001192092896},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3111000061035156},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3034000098705292},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.26170000433921814},{"id":"https://openalex.org/C144559511","wikidata":"https://www.wikidata.org/wiki/Q2986279","display_name":"Principal (computer security)","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.08588","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08588","pdf_url":"https://arxiv.org/pdf/2510.08588","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.08588","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.08588","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.08588","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08588","pdf_url":"https://arxiv.org/pdf/2510.08588","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Biomedical":[0],"Named":[1],"Entity":[2],"Recognition":[3],"(BioNER),":[4],"task6":[5],"in":[6,10],"BioASQ":[7,48],"(A":[8],"challenge":[9,137],"large-scale":[11],"biomedical":[12],"semantic":[13],"indexing":[14],"and":[15,38,50,143],"question":[16],"answering),":[17],"is":[18],"crucial":[19],"for":[20,129,150],"extracting":[21],"information":[22],"from":[23,76,113],"scientific":[24],"literature":[25],"but":[26,133],"faces":[27],"hurdles":[28],"such":[29],"as":[30],"distinguishing":[31],"between":[32],"similar":[33],"entity":[34],"types":[35],"like":[36],"genes":[37],"chemicals.":[39],"This":[40,121],"study":[41],"evaluates":[42],"the":[43,73,89,94,105,124,135,144],"GLiNER-BioMed":[44],"model":[45,96],"on":[46,68],"a":[47,52,77,98],"dataset":[49],"introduces":[51],"targeted":[53],"dictionary-based":[54,127],"post-processing":[55,63],"strategy":[56],"to":[57,81,88,104,140],"address":[58],"common":[59],"misclassifications.":[60],"While":[61],"this":[62,83],"approach":[64],"demonstrated":[65],"notable":[66],"improvement":[67],"our":[69],"development":[70,141],"set,":[71,92],"increasing":[72],"micro":[74,99],"F1-score":[75,100],"baseline":[78],"of":[79,101,126,138,146],"0.79":[80],"0.83,":[82],"enhancement":[84],"did":[85],"not":[86],"generalize":[87],"blind":[90],"test":[91],"where":[93],"post-processed":[95],"achieved":[97],"0.77":[102],"compared":[103],"baselines":[106],"0.79.":[107],"We":[108],"also":[109],"discuss":[110],"insights":[111],"gained":[112],"exploring":[114],"alternative":[115],"methodologies,":[116],"including":[117],"Conditional":[118],"Random":[119],"Fields.":[120],"work":[122],"highlights":[123],"potential":[125],"refinement":[128],"pre-trained":[130],"BioNER":[131],"models":[132],"underscores":[134],"critical":[136],"overfitting":[139],"data":[142],"necessity":[145],"ensuring":[147],"robust":[148],"generalization":[149],"real-world":[151],"applicability.":[152]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-14T00:00:00"}
