{"id":"https://openalex.org/W7166837673","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1560","title":"BCL: Bayesian In-Context Learning Framework for Information Extraction","display_name":"BCL: Bayesian In-Context Learning Framework for Information Extraction","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166837673","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1560"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.1560","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1560","pdf_url":"https://aclanthology.org/2026.findings-acl.1560.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":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.1560.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070514010","display_name":"Haoliang Liu","orcid":"https://orcid.org/0000-0001-9802-1202"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haoliang Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139783036","display_name":"Chengkun Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengkun Cai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139777850","display_name":"Xu Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu Zhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139793336","display_name":"Han Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han Zhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087643562","display_name":"Shizhou Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shizhou Huang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019726320","display_name":"Xinglin Zhang","orcid":"https://orcid.org/0000-0003-2592-6945"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinglin Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139753290","display_name":"Tao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139838644","display_name":"Jenq-Neng Hwang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jenq-Neng Hwang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139807229","display_name":"Zhang Huaping","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang Huaping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139778197","display_name":"Lei Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.82716246,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"31172","last_page":"31189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.2076999992132187,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.2076999992132187,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.12839999794960022,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.07129999995231628,"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/bayesian-probability","display_name":"Bayesian probability","score":0.48170000314712524},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4320000112056732},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32739999890327454},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.2935999929904938},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2888999879360199},{"id":"https://openalex.org/keywords/prior-information","display_name":"Prior information","score":0.2809000015258789}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6383000016212463},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6365000009536743},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48170000314712524},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39750000834465027},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35040000081062317},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.1560","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1560","pdf_url":"https://aclanthology.org/2026.findings-acl.1560.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":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.1560","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1560","pdf_url":"https://aclanthology.org/2026.findings-acl.1560.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":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166837673.pdf","grobid_xml":"https://content.openalex.org/works/W7166837673.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Existing":[0],"information":[1],"extraction":[2],"(IE)":[3],"tasks":[4],"increasingly":[5],"adopt":[6],"in-context":[7],"learning":[8],"(ICL)":[9],"with":[10,49],"large":[11],"language":[12],"models.However,":[13],"current":[14],"approaches":[15],"either":[16],"show":[17],"inconsistent":[18],"performance":[19],"across":[20,57],"model":[21],"scales":[22],"or":[23],"lack":[24],"systematic":[25],"optimization":[26,43],"and":[27,65,73,80],"generalizability.Building":[28],"on":[29],"this,":[30],"we":[31],"propose":[32],"BCL":[33,67],"(Bayesian":[34],"In-Context":[35],"Learning":[36],"Framework":[37],"for":[38],"Information":[39],"Extraction),":[40],"the":[41],"first":[42],"framework":[44],"that":[45],"uses":[46],"particle":[47],"filtering":[48],"Bayesian":[50],"updates":[51],"to":[52,69],"systematically":[53],"refine":[54],"label":[55],"representations":[56],"IE":[58],"tasks.Through":[59],"four":[60],"steps-initialization,":[61],"observation,":[62],"weight":[63],"update,":[64],"resampling,":[66],"generalizes":[68],"both":[70],"sequence":[71],"labeling":[72],"relation":[74],"classification":[75],"paradigms.Extensive":[76],"experiments":[77],"demonstrate":[78],"substantial":[79],"consistent":[81],"improvements":[82],"over":[83],"existing":[84],"approaches.":[85]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}
