{"id":"https://openalex.org/W3116645343","doi":"https://doi.org/10.18653/v1/2020.coling-main.265","title":"Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification","display_name":"Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3116645343","doi":"https://doi.org/10.18653/v1/2020.coling-main.265","mag":"3116645343"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2020.coling-main.265","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.coling-main.265","pdf_url":"https://www.aclweb.org/anthology/2020.coling-main.265.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 28th International Conference on Computational Linguistics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/2020.coling-main.265.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070414569","display_name":"Juan Li","orcid":"https://orcid.org/0000-0002-1046-5325"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juan Li","raw_affiliation_strings":["AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","Zhejiang University"],"affiliations":[{"raw_affiliation_string":"AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","institution_ids":[]},{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086280314","display_name":"Ruoxu Wang","orcid":"https://orcid.org/0000-0002-7088-5954"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruoxu Wang","raw_affiliation_strings":["AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","Zhejiang University"],"affiliations":[{"raw_affiliation_string":"AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","institution_ids":[]},{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089259739","display_name":"Ningyu Zhang","orcid":"https://orcid.org/0000-0002-1970-0678"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ningyu Zhang","raw_affiliation_strings":["AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","Zhejiang University"],"affiliations":[{"raw_affiliation_string":"AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","institution_ids":[]},{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100448121","display_name":"Wen Zhang","orcid":"https://orcid.org/0000-0001-5221-2628"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Zhang","raw_affiliation_strings":["AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","Zhejiang University"],"affiliations":[{"raw_affiliation_string":"AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","institution_ids":[]},{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012430644","display_name":"Fan Yang","orcid":"https://orcid.org/0000-0003-0458-4325"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Yang","raw_affiliation_strings":["AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","Zhejiang University"],"affiliations":[{"raw_affiliation_string":"AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","institution_ids":[]},{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102018239","display_name":"Huajun Chen","orcid":"https://orcid.org/0000-0001-5496-7442"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huajun Chen","raw_affiliation_strings":["AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","Zhejiang University"],"affiliations":[{"raw_affiliation_string":"AZFT Joint Lab for Knowledge Engine {lijuan18,ruoxuwang,zhangningyu,wenzhang2015,21821249,","institution_ids":[]},{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5102018239"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":2.9902,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.93007049,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2967","last_page":"2978"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"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":0.9997000098228455,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9965999722480774,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9945999979972839,"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/relation","display_name":"Relation (database)","score":0.8025797605514526},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.69803786277771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6575117707252502},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6353554725646973},{"id":"https://openalex.org/keywords/logical-consequence","display_name":"Logical consequence","score":0.5040251612663269},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.4593195915222168},{"id":"https://openalex.org/keywords/semantic-relation","display_name":"Semantic relation","score":0.45458683371543884},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43824347853660583},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3326337933540344},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.13736170530319214}],"concepts":[{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.8025797605514526},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.69803786277771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6575117707252502},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6353554725646973},{"id":"https://openalex.org/C134752490","wikidata":"https://www.wikidata.org/wiki/Q374182","display_name":"Logical consequence","level":2,"score":0.5040251612663269},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.4593195915222168},{"id":"https://openalex.org/C2988080768","wikidata":"https://www.wikidata.org/wiki/Q7095057","display_name":"Semantic relation","level":3,"score":0.45458683371543884},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43824347853660583},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3326337933540344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.13736170530319214},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2020.coling-main.265","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.coling-main.265","pdf_url":"https://www.aclweb.org/anthology/2020.coling-main.265.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 28th International Conference on Computational Linguistics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2020.coling-main.265","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.coling-main.265","pdf_url":"https://www.aclweb.org/anthology/2020.coling-main.265.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 28th International Conference on Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8399999737739563,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G19292306","display_name":null,"funder_award_id":"U19B2027","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3961709845","display_name":null,"funder_award_id":"NSFC91846204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4892364866","display_name":null,"funder_award_id":"61473260","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5452219684","display_name":null,"funder_award_id":"2018YFB1402800","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5616351183","display_name":null,"funder_award_id":"00000","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7567925786","display_name":null,"funder_award_id":"91846204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8799172931","display_name":null,"funder_award_id":"B14028","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8847056732","display_name":null,"funder_award_id":"91846204/U19B2027","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"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3116645343.pdf","grobid_xml":"https://content.openalex.org/works/W3116645343.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W93016980","https://openalex.org/W652269744","https://openalex.org/W1426956448","https://openalex.org/W1533230146","https://openalex.org/W2053238041","https://openalex.org/W2098411764","https://openalex.org/W2107306718","https://openalex.org/W2109933951","https://openalex.org/W2123024445","https://openalex.org/W2127795553","https://openalex.org/W2145544171","https://openalex.org/W2150295085","https://openalex.org/W2151502664","https://openalex.org/W2153579005","https://openalex.org/W2171061940","https://openalex.org/W2184957013","https://openalex.org/W2210714229","https://openalex.org/W2250342289","https://openalex.org/W2251135946","https://openalex.org/W2545740386","https://openalex.org/W2587809655","https://openalex.org/W2604314403","https://openalex.org/W2612900438","https://openalex.org/W2728059831","https://openalex.org/W2756566873","https://openalex.org/W2777746208","https://openalex.org/W2808051209","https://openalex.org/W2885711033","https://openalex.org/W2890299555","https://openalex.org/W2899641520","https://openalex.org/W2905461678","https://openalex.org/W2919278763","https://openalex.org/W2949972983","https://openalex.org/W2955116803","https://openalex.org/W2962756504","https://openalex.org/W2962869320","https://openalex.org/W2962881743","https://openalex.org/W2963360413","https://openalex.org/W2963655104","https://openalex.org/W2963718112","https://openalex.org/W2964116313","https://openalex.org/W2964140943","https://openalex.org/W2970823335","https://openalex.org/W2991468406","https://openalex.org/W2997262687","https://openalex.org/W3012584427","https://openalex.org/W3038105747","https://openalex.org/W3084874487","https://openalex.org/W3085225979","https://openalex.org/W3086340792","https://openalex.org/W3088717877","https://openalex.org/W3099910226","https://openalex.org/W3175985340","https://openalex.org/W4249965322","https://openalex.org/W4288087297","https://openalex.org/W4294170691","https://openalex.org/W4299547686"],"related_works":["https://openalex.org/W4234874385","https://openalex.org/W2350997567","https://openalex.org/W2392969287","https://openalex.org/W2370384704","https://openalex.org/W2357087812","https://openalex.org/W2888033806","https://openalex.org/W2014288545","https://openalex.org/W112768223","https://openalex.org/W2976808399","https://openalex.org/W2739730962"],"abstract_inverted_index":{"Relation":[0],"classification":[1],"aims":[2],"to":[3,55,61,125],"extract":[4],"semantic":[5,70,86,107],"relations":[6,30,102],"between":[7,98],"entity":[8],"pairs":[9],"from":[10],"the":[11,36,45,63,73],"sentences.":[12],"However,":[13],"most":[14],"existing":[15],"methods":[16],"can":[17,123],"only":[18],"identify":[19],"seen":[20,99],"relation":[21,40,66,74,92,127],"classes":[22],"that":[23,120],"occurred":[24],"during":[25],"training.":[26],"To":[27],"recognize":[28],"unseen":[29,101,126],"at":[31],"test":[32],"time,":[33],"we":[34,81],"explore":[35],"problem":[37,46],"of":[38,65,72],"zero-shot":[39,91],"classification.":[41,93],"Previous":[42],"work":[43],"regards":[44],"as":[47],"reading":[48],"comprehension":[49],"or":[50],"textual":[51],"entailment,":[52],"which":[53],"have":[54],"rely":[56],"on":[57],"artificial":[58],"descriptive":[59],"information":[60],"improve":[62],"understandability":[64],"types.":[67],"Thus,":[68],"rich":[69],"knowledge":[71,110],"labels":[75],"is":[76],"ignored.":[77],"In":[78],"this":[79],"paper,":[80],"propose":[82],"a":[83],"novel":[84],"logic-guided":[85],"representation":[87],"learning":[88],"model":[89],"for":[90],"Our":[94],"approach":[95],"builds":[96],"connections":[97],"and":[100,105,113,129],"via":[103],"implicit":[104],"explicit":[106],"representations":[108],"with":[109],"graph":[111],"embeddings":[112],"logic":[114],"rules.":[115],"Extensive":[116],"experimental":[117],"results":[118],"demonstrate":[119],"our":[121],"method":[122],"generalize":[124],"types":[128],"achieve":[130],"promising":[131],"improvements.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
