{"id":"https://openalex.org/W4407385281","doi":"https://doi.org/10.48550/arxiv.2502.05704","title":"Rethinking Word Similarity: Semantic Similarity through Classification Confusion","display_name":"Rethinking Word Similarity: Semantic Similarity through Classification Confusion","publication_year":2025,"publication_date":"2025-02-08","ids":{"openalex":"https://openalex.org/W4407385281","doi":"https://doi.org/10.48550/arxiv.2502.05704"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2502.05704","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.05704","pdf_url":"https://arxiv.org/pdf/2502.05704","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/2502.05704","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031278789","display_name":"Kaitlyn Zhou","orcid":"https://orcid.org/0000-0001-8804-8161"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhou, Kaitlyn","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018995016","display_name":"Haishan Gao","orcid":"https://orcid.org/0000-0002-4954-8793"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Haishan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022333508","display_name":"Sarah Chen","orcid":"https://orcid.org/0000-0002-9416-8191"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Sarah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091574623","display_name":"Dan Edelstein","orcid":"https://orcid.org/0000-0002-2541-1119"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Edelstein, Dan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087088138","display_name":"Dan Jurafsky","orcid":"https://orcid.org/0000-0002-6459-7745"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jurafsky, Dan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5028154693","display_name":"Chen Shani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shani, Chen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5031278789"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.8116999864578247,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.8116999864578247,"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/T10028","display_name":"Topic Modeling","score":0.7434999942779541,"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/semantic-similarity","display_name":"Semantic similarity","score":0.7372928261756897},{"id":"https://openalex.org/keywords/confusion","display_name":"Confusion","score":0.7298406958580017},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7287452816963196},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.651098370552063},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6277602314949036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5450659990310669},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4811635911464691},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.46985211968421936},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.24205446243286133},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.09284213185310364}],"concepts":[{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.7372928261756897},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.7298406958580017},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7287452816963196},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.651098370552063},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6277602314949036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5450659990310669},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4811635911464691},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.46985211968421936},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.24205446243286133},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.09284213185310364},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2502.05704","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.05704","pdf_url":"https://arxiv.org/pdf/2502.05704","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.2502.05704","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2502.05704","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2502.05704","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.05704","pdf_url":"https://arxiv.org/pdf/2502.05704","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":[{"id":"https://openalex.org/G4579206564","display_name":null,"funder_award_id":"IIS-2128145","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5921281487","display_name":null,"funder_award_id":"number","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6755165505","display_name":null,"funder_award_id":"award","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8935263488","display_name":"RI: Small: New tools for studying structural and inductive bias in NLP models","funder_award_id":"2128145","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4407385281.pdf","grobid_xml":"https://content.openalex.org/works/W4407385281.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4376653378","https://openalex.org/W2114797768","https://openalex.org/W2380654781","https://openalex.org/W2296205523","https://openalex.org/W2176214140","https://openalex.org/W2516873349","https://openalex.org/W4385239468","https://openalex.org/W4200476258","https://openalex.org/W2022470916","https://openalex.org/W3201962287"],"abstract_inverted_index":{"Word":[0,52,64],"similarity":[1,26,31,57,72,114,136,140,152,203],"has":[2],"many":[3],"applications":[4],"to":[5,82,86,134,163,172,190],"social":[6,226],"science":[7,227],"and":[8,18,89,117,147,149,216,228,231],"cultural":[9,229],"analytics":[10,230],"tasks":[11],"like":[12],"measuring":[13],"meaning":[14,182],"change":[15],"over":[16],"time":[17],"making":[19],"sense":[20],"of":[21,42,50,60,96,103,112,115,121,156,166,183,201,208,219,224],"contested":[22],"terms.":[23],"Yet":[24],"traditional":[25],"methods":[27],"based":[28],"on":[29],"cosine":[30,135],"between":[32],"word":[33,87,100,107,186],"embeddings":[34,85],"cannot":[35],"capture":[36,213],"the":[37,91,104,113,127,179,184,194,206,214,222],"context-dependent,":[38],"asymmetrical,":[39],"polysemous":[40],"nature":[41,218],"semantic":[43,56,202],"similarity.":[44],"We":[45,158,197],"propose":[46],"a":[47,80,98,110,174],"new":[48,209],"measure":[49,111,151],"similarity,":[51],"Confusion,":[53],"that":[54,71,211],"reframes":[55],"in":[58,137,178],"terms":[59],"feature-based":[61],"classification":[62],"confusion.":[63],"Confusion":[65],"is":[66,132],"inspired":[67],"by":[68,169],"Tversky's":[69],"suggestion":[70],"features":[73,155,168],"be":[74],"chosen":[75,128],"dynamically.":[76],"Here":[77],"we":[78],"train":[79],"classifier":[81,92],"map":[83],"contextual":[84],"identities":[88],"use":[90,165],"confusion":[93],"(the":[94],"probability":[95],"choosing":[97],"confounding":[99,123],"c":[101,116],"instead":[102],"correct":[105],"target":[106],"t)":[108],"as":[109,126],"t.":[118],"The":[119],"set":[120],"potential":[122],"words":[124],"acts":[125],"features.":[129],"Our":[130],"method":[131],"comparable":[133],"matching":[138],"human":[139],"judgments":[141],"across":[142],"several":[143],"datasets":[144],"(MEN,":[145],"WirdSim353,":[146],"SimLex),":[148],"can":[150],"using":[153],"predetermined":[154],"interest.":[157],"demonstrate":[159],"our":[160],"model's":[161],"ability":[162],"make":[164],"dynamic":[167,217],"applying":[170],"it":[171],"test":[173],"hypothesis":[175],"about":[176],"changes":[177],"18th":[180],"C.":[181],"French":[185,195],"\"revolution\"":[187],"from":[188],"popular":[189],"state":[191],"action":[192],"during":[193],"Revolution.":[196],"hope":[198],"this":[199],"reimagining":[200],"will":[204],"inspire":[205],"development":[207],"tools":[210],"better":[212],"multi-faceted":[215],"language,":[220],"advancing":[221],"fields":[223],"computational":[225],"beyond.":[232]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}