{"id":"https://openalex.org/W7140121318","doi":"https://doi.org/10.48550/arxiv.2603.19825","title":"FrameNet Semantic Role Classification by Analogy","display_name":"FrameNet Semantic Role Classification by Analogy","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7140121318","doi":"https://doi.org/10.48550/arxiv.2603.19825"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19825","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19825","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.19825","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124994752","display_name":"Van-Duy Ngo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ngo, Van-Duy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078178804","display_name":"Stergos Afantenos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Afantenos, Stergos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037462924","display_name":"Emiliano Lorini","orcid":"https://orcid.org/0000-0002-7014-6756"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lorini, Emiliano","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066732273","display_name":"Miguel Couceiro","orcid":"https://orcid.org/0000-0003-2316-7623"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Couceiro, Miguel","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":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/T10028","display_name":"Topic Modeling","score":0.17810000479221344,"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":0.17810000479221344,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1509000062942505,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.11289999634027481,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/framenet","display_name":"FrameNet","score":0.571399986743927},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5317999720573425},{"id":"https://openalex.org/keywords/analogy","display_name":"Analogy","score":0.4724000096321106},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.46380001306533813},{"id":"https://openalex.org/keywords/transitive-relation","display_name":"Transitive relation","score":0.43970000743865967},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4185999929904938},{"id":"https://openalex.org/keywords/semantic-compression","display_name":"Semantic compression","score":0.4065000116825104},{"id":"https://openalex.org/keywords/binary-relation","display_name":"Binary relation","score":0.3944000005722046},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3919000029563904},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.38909998536109924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7034000158309937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7016000151634216},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6223999857902527},{"id":"https://openalex.org/C185754541","wikidata":"https://www.wikidata.org/wiki/Q1322093","display_name":"FrameNet","level":3,"score":0.571399986743927},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5317999720573425},{"id":"https://openalex.org/C521332185","wikidata":"https://www.wikidata.org/wiki/Q185816","display_name":"Analogy","level":2,"score":0.4724000096321106},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C191399111","wikidata":"https://www.wikidata.org/wiki/Q64861","display_name":"Transitive relation","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4185999929904938},{"id":"https://openalex.org/C202708506","wikidata":"https://www.wikidata.org/wiki/Q7449050","display_name":"Semantic compression","level":5,"score":0.4065000116825104},{"id":"https://openalex.org/C65180967","wikidata":"https://www.wikidata.org/wiki/Q130901","display_name":"Binary relation","level":2,"score":0.3944000005722046},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.37770000100135803},{"id":"https://openalex.org/C65236422","wikidata":"https://www.wikidata.org/wiki/Q173740","display_name":"Cartesian product","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C200288055","wikidata":"https://www.wikidata.org/wiki/Q2621792","display_name":"Element (criminal law)","level":2,"score":0.3598000109195709},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C67277372","wikidata":"https://www.wikidata.org/wiki/Q7449085","display_name":"Semantic role labeling","level":3,"score":0.3310999870300293},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.328900009393692},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C37926939","wikidata":"https://www.wikidata.org/wiki/Q7449061","display_name":"Semantic equivalence","level":4,"score":0.32019999623298645},{"id":"https://openalex.org/C198942812","wikidata":"https://www.wikidata.org/wiki/Q496618","display_name":"Semantic property","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.2922999858856201},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2736000120639801},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19825","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19825","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.19825","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19825","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":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"In":[0],"this":[1,49],"paper,":[2],"we":[3,39],"adopt":[4],"a":[5,43,55,86,129],"relational":[6],"view":[7],"of":[8,27,48,124],"analogies":[9,19],"applied":[10],"to":[11,41,76,106,142],"Semantic":[12,78,101],"Role":[13,79,102],"Classification":[14,80],"in":[15],"FrameNet.":[16],"We":[17,112],"define":[18],"as":[20,54,69],"formal":[21],"relations":[22],"over":[23,122],"the":[24,60,64,107],"Cartesian":[25],"product":[26],"frame":[28,34,61,131],"evoking":[29],"lexical":[30],"units":[31],"(LUs)":[32],"and":[33,84,135,151],"element":[35,47],"(FEs)":[36],"pairs,":[37],"which":[38],"use":[40],"construct":[42],"new":[44],"dataset.":[45],"Each":[46],"binary":[50,82],"relation":[51],"is":[52,104],"labelled":[53],"valid":[56],"analogical":[57,136],"instance":[58],"if":[59],"elements":[62],"share":[63],"same":[65],"semantic":[66,114,126],"role,":[67],"or":[68],"invalid":[70],"otherwise.":[71],"This":[72,138],"formulation":[73],"allows":[74,140],"us":[75,141],"transform":[77],"into":[81],"classification":[83],"train":[85],"lightweight":[87],"Artificial":[88],"Neural":[89],"Network":[90],"(ANN)":[91],"that":[92],"exhibits":[93],"rapid":[94],"convergence":[95],"with":[96],"minimal":[97],"parameters.":[98],"Unconventionally,":[99],"no":[100],"information":[103],"introduced":[105],"neural":[108],"network":[109],"during":[110,116],"training.":[111],"recover":[113],"roles":[115,127],"inference":[117],"by":[118],"computing":[119],"probability":[120],"distributions":[121],"candidates":[123],"all":[125],"within":[128],"given":[130],"through":[132],"random":[133],"sampling":[134],"transfer.":[137],"approach":[139],"surpass":[143],"previous":[144],"state-of-the-art":[145],"results":[146],"while":[147],"maintaining":[148],"computational":[149],"efficiency":[150],"frugality.":[152]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-24T00:00:00"}