{"id":"https://openalex.org/W4416036035","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.106","title":"Towards Achieving Concept Completeness for Textual Concept Bottleneck Models","display_name":"Towards Achieving Concept Completeness for Textual Concept Bottleneck Models","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416036035","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.106"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.106","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.106","pdf_url":"https://aclanthology.org/2025.findings-emnlp.106.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: EMNLP 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-emnlp.106.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063911894","display_name":"Milan Bhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Milan Bhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116308803","display_name":"Yann Choho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yann Choho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009688030","display_name":"Jean-No\u00ebl Vittaut","orcid":"https://orcid.org/0000-0001-6654-4199"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jean-No\u00ebl Vittaut","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060566931","display_name":"Nicolas Chesneau","orcid":"https://orcid.org/0000-0002-8313-5829"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicolas Chesneau","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069789840","display_name":"Pierre Moreau","orcid":"https://orcid.org/0000-0002-3604-721X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pierre Moreau","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075900406","display_name":"Marie\u2010Jeanne Lesot","orcid":"https://orcid.org/0000-0002-3604-6647"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marie-Jeanne Lesot","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7588,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.89429999,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2007","last_page":"2024"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.14790000021457672,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.14790000021457672,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.13809999823570251,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.10719999670982361,"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/completeness","display_name":"Completeness (order theory)","score":0.6480000019073486},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.43880000710487366},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2809000015258789},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.26420000195503235}],"concepts":[{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.6480000019073486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6266000270843506},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38190001249313354},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32260000705718994},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28760001063346863},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26809999346733093},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.106","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.106","pdf_url":"https://aclanthology.org/2025.findings-emnlp.106.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: EMNLP 2025","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-05478263v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05478263","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://aclanthology.org/2025.findings-emnlp.106/","raw_type":"Conference papers"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.106","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.106","pdf_url":"https://aclanthology.org/2025.findings-emnlp.106.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: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416036035.pdf","grobid_xml":"https://content.openalex.org/works/W4416036035.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Textual":[0,26],"Concept":[1,27],"Bottleneck":[2,28],"Models":[3],"(TCBMs)":[4],"are":[5],"interpretable-by-design":[6],"models":[7],"for":[8,52],"text":[9],"classification":[10],"that":[11],"predict":[12],"a":[13,31,39,44,74,94],"set":[14],"of":[15,85,101],"salient":[16],"concepts":[17,56,67],"before":[18],"making":[19],"the":[20,50,69],"final":[21],"prediction.This":[22],"paper":[23],"proposes":[24],"Complete":[25],"Model":[29],"(CT-CBM),":[30],"novel":[32],"TCBM":[33],"generator":[34],"building":[35],"concept":[36,76,86,90],"labels":[37],"in":[38,68,83],"fully":[40],"unsupervised":[41],"manner":[42],"using":[43],"small":[45],"language":[46],"model,":[47],"eliminating":[48],"both":[49],"need":[51],"predefined":[53],"human":[54],"labeled":[55],"and":[57,62,65,89],"LLM":[58],"annotations.CT-CBM":[59],"iteratively":[60],"targets":[61],"adds":[63],"important":[64],"identifiable":[66],"bottleneck":[70],"layer":[71],"to":[72,97],"create":[73],"complete":[75],"basis.CT-CBM":[77],"achieves":[78],"striking":[79],"results":[80],"against":[81],"competitors":[82],"terms":[84],"basis":[87],"completeness":[88],"detection":[91],"accuracy,":[92],"offering":[93],"promising":[95],"solution":[96],"reliably":[98],"enhance":[99],"interpretability":[100],"NLP":[102],"classifiers.":[103]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-08T00:00:00"}
