{"id":"https://openalex.org/W7166866097","doi":"https://doi.org/10.18653/v1/2026.acl-long.898","title":"NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning","display_name":"NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166866097","doi":"https://doi.org/10.18653/v1/2026.acl-long.898"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.acl-long.898","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.898","pdf_url":"https://aclanthology.org/2026.acl-long.898.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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.acl-long.898.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133595546","display_name":"Yanyi Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanyi Su","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133571454","display_name":"Hongshuai Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongshuai Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139795158","display_name":"Zhifeng Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhifeng Gao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139728717","display_name":"Jun Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jun Cheng","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":0.9,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"19615","last_page":"19647"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.004900000058114529,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.0024999999441206455,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43650001287460327},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.43209999799728394},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.375900000333786},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3630000054836273},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.3305000066757202}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.604200005531311},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5745999813079834},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43650001287460327},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.43209999799728394},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3125999867916107},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3070000112056732},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27149999141693115},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.acl-long.898","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.898","pdf_url":"https://aclanthology.org/2026.acl-long.898.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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.acl-long.898","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.898","pdf_url":"https://aclanthology.org/2026.acl-long.898.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 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.455260306596756,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1152808041","display_name":null,"funder_award_id":"AI4EC","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G1732190671","display_name":null,"funder_award_id":"22225302","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1879160186","display_name":null,"funder_award_id":"92470201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5003051561","display_name":null,"funder_award_id":"22021001","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G5349271303","display_name":null,"funder_award_id":"92461312","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7327409615","display_name":null,"funder_award_id":"22021001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166866097.pdf","grobid_xml":"https://content.openalex.org/works/W7166866097.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Olfaction":[0],"lies":[1],"at":[2],"the":[3,30,34,70,94,102,125,141],"intersection":[4],"of":[5,29,98,104,121],"chemical":[6],"structure,":[7,74],"neural":[8],"encoding,":[9],"and":[10,53,77,136,148,152],"linguistic":[11,42],"perception,":[12],"yet":[13],"existing":[14],"representation":[15,62,146],"methods":[16],"fail":[17],"to":[18,39,41,114],"fully":[19],"capture":[20],"this":[21],"pathway.Current":[22],"approaches":[23],"typically":[24],"model":[25],"only":[26],"isolated":[27],"segments":[28],"olfactory":[31,71,105,150],"pathway,":[32],"overlooking":[33],"complete":[35],"chain":[36],"from":[37],"molecule":[38],"receptors":[40],"descriptions.Such":[43],"fragmentation":[44],"yields":[45],"learned":[46],"embeddings":[47],"that":[48,65,130],"lack":[49],"both":[50],"biological":[51],"grounding":[52],"semantic":[54,116],"interpretability.We":[55],"propose":[56],"NOSE":[57,131],"(Neural":[58],"Olfactory-Semantic":[59],"Embedding),":[60],"a":[61,109],"learning":[63],"framework":[64],"aligns":[66],"three":[67],"modalities":[68],"along":[69],"pathway:":[72],"molecular":[73],"receptor":[75],"sequence,":[76],"natural":[78],"language":[79],"description.Rather":[80],"than":[81],"simply":[82],"fusing":[83],"these":[84],"signals,":[85],"we":[86,107],"decouple":[87],"their":[88],"contributions":[89],"via":[90],"orthogonal":[91],"constraints,":[92],"preserving":[93],"unique":[95],"encoded":[96],"information":[97],"each":[99],"modality.To":[100],"address":[101],"sparsity":[103],"language,":[106],"introduce":[108],"weak":[110],"positive":[111],"sample":[112],"strategy":[113],"calibrate":[115],"similarity,":[117],"preventing":[118],"erroneous":[119],"repulsion":[120],"similar":[122],"odors":[123],"in":[124],"feature":[126],"space.Extensive":[127],"experiments":[128],"demonstrate":[129],"achieves":[132],"state-of-the-art":[133],"(SOTA)":[134],"performance":[135],"excellent":[137],"zero-shot":[138],"generalization,":[139],"confirming":[140],"strong":[142],"alignment":[143],"between":[144],"its":[145],"space":[147],"human":[149],"intuition.Code":[151]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}
