{"id":"https://openalex.org/W4292950893","doi":"https://doi.org/10.48550/arxiv.2208.10081","title":"Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing","display_name":"Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing","publication_year":2022,"publication_date":"2022-08-22","ids":{"openalex":"https://openalex.org/W4292950893","doi":"https://doi.org/10.48550/arxiv.2208.10081"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2208.10081","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2208.10081","pdf_url":"https://arxiv.org/pdf/2208.10081","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2208.10081","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004591174","display_name":"Xinyu Zuo","orcid":"https://orcid.org/0009-0008-0390-0414","observed_orcids":["https://orcid.org/0009-0008-0390-0414"]},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zuo, Xinyu","raw_affiliation_strings":["Tencent Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048582949","display_name":"Haijin Liang","orcid":"https://orcid.org/0009-0006-3464-9192","observed_orcids":["https://orcid.org/0009-0006-3464-9192"]},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang, Haijin","raw_affiliation_strings":["Tencent Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jing, Ning","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing, Ning","raw_affiliation_strings":["Tencent Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109440703","display_name":"Shuang Zeng","orcid":"https://orcid.org/0009-0004-1936-3802","observed_orcids":["https://orcid.org/0009-0004-1936-3802","https://orcid.org/0009-0004-4477-8532"]},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeng, Shuang","raw_affiliation_strings":["Tencent Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fang, Zhou","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang, Zhou","raw_affiliation_strings":["Tencent Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100440778","display_name":"Yu Luo","orcid":"https://orcid.org/0000-0001-6229-4639","observed_orcids":["https://orcid.org/0000-0001-6229-4639"]},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luo, Yu","raw_affiliation_strings":["Tencent Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":4,"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":0.9965999722480774,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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":0.00139999995008111,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/fine-grained-entity-typing","display_name":"fine-grained entity typing","score":1.0},{"id":"https://openalex.org/keywords/contrastive-learning","display_name":"contrastive learning","score":0.8579999804496765},{"id":"https://openalex.org/keywords/entity-mentions","display_name":"entity mentions","score":0.4309999942779541},{"id":"https://openalex.org/keywords/semantic-types","display_name":"semantic types","score":0.609000027179718}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7839162349700928},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.7469338774681091},{"id":"https://openalex.org/C2777299769","wikidata":"https://www.wikidata.org/wiki/Q3707858","display_name":"Type (biology)","level":2,"score":0.6966490149497986},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.49773290753364563},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.454620897769928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4218023121356964},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.1099226176738739},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.07926797866821289},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2208.10081","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2208.10081","pdf_url":"https://arxiv.org/pdf/2208.10081","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2208.10081","doi":"https://doi.org/10.48550/arxiv.2208.10081","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2208.10081","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2208.10081","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2208.10081","pdf_url":"https://arxiv.org/pdf/2208.10081","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4292950893.pdf","grobid_xml":"https://content.openalex.org/works/W4292950893.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W2132096166","https://openalex.org/W2294414650","https://openalex.org/W2887997457","https://openalex.org/W2949712338","https://openalex.org/W2963085936","https://openalex.org/W3173854146","https://openalex.org/W3195893957","https://openalex.org/W4225716497","https://openalex.org/W4285249984","https://openalex.org/W4285604861"],"related_works":["https://openalex.org/W2963085936","https://openalex.org/W4411372143","https://openalex.org/W3022277486","https://openalex.org/W7166828836","https://openalex.org/W2770431247","https://openalex.org/W4210900105","https://openalex.org/W4411630248","https://openalex.org/W4401863425","https://openalex.org/W4224273950","https://openalex.org/W3187346832"],"abstract_inverted_index":{"Fine-grained":[0],"entity":[1,12,92],"typing":[2],"(FET)":[3],"aims":[4],"to":[5,79,94,114],"deduce":[6],"specific":[7],"semantic":[8],"types":[9,74,127],"of":[10],"the":[11,35,43,70,77,85,101,111,117,124],"mentions":[13],"in":[14],"text.":[15],"Modern":[16],"methods":[17],"for":[18,63],"FET":[19,148],"mainly":[20],"focus":[21],"on":[22,110,133,147],"learning":[23],"what":[24],"a":[25,58,106],"certain":[26],"type":[27,36,47,90,96,118,152],"looks":[28],"like.":[29],"And":[30],"few":[31],"works":[32],"directly":[33,68,98,115],"model":[34,69,116],"differences,":[37,119],"that":[38,45,141],"is,":[39],"let":[40],"models":[41],"know":[42],"extent":[44],"one":[46,86],"is":[48],"different":[49,129],"from":[50],"others.":[51],"To":[52],"alleviate":[53],"this":[54],"problem,":[55],"we":[56,88,104],"propose":[57],"type-enriched":[59],"hierarchical":[60,73,112],"contrastive":[61,108],"strategy":[62,109],"FET.":[64],"Our":[65],"method":[66,143],"can":[67,121],"differences":[71],"between":[72,126],"and":[75,138],"improve":[76],"ability":[78],"distinguish":[80],"multi-grained":[81],"similar":[82],"types.":[83],"On":[84,100],"hand,":[87,103],"embed":[89],"into":[91],"contexts":[93],"make":[95],"information":[97],"perceptible.":[99],"other":[102],"design":[105],"constrained":[107],"structure":[113],"which":[120],"simultaneously":[122],"perceive":[123],"distinguishability":[125],"at":[128],"granularity.":[130],"Experimental":[131],"results":[132],"three":[134],"benchmarks,":[135],"BBN,":[136],"OntoNotes,":[137],"FIGER":[139],"show":[140],"our":[142],"achieves":[144],"significant":[145],"performance":[146],"by":[149],"effectively":[150],"modeling":[151],"differences.":[153]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2022-08-24T00:00:00"}
