{"id":"https://openalex.org/W4389519411","doi":"https://doi.org/10.18653/v1/2023.emnlp-main.170","title":"GLEN: General-Purpose Event Detection for Thousands of Types","display_name":"GLEN: General-Purpose Event Detection for Thousands of Types","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389519411","doi":"https://doi.org/10.18653/v1/2023.emnlp-main.170"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2023.emnlp-main.170","doi":"https://doi.org/10.18653/v1/2023.emnlp-main.170","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.emnlp-main.170","pdf_url":"https://aclanthology.org/2023.emnlp-main.170.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 2023 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2023.emnlp-main.170.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100402721","display_name":"Sha Li","orcid":"https://orcid.org/0000-0003-4598-9740","observed_orcids":["https://orcid.org/0000-0003-4598-9740"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sha Li","raw_affiliation_strings":["University of Illinois Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090006261","display_name":"Qiusi Zhan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiusi Zhan","raw_affiliation_strings":["University of Illinois Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008706150","display_name":"Kathryn Conger","orcid":"https://orcid.org/0000-0002-9949-4025","observed_orcids":["https://orcid.org/0000-0002-9949-4025"]},"institutions":[{"id":"https://openalex.org/I188538660","display_name":"University of Colorado Boulder","ror":"https://ror.org/02ttsq026","country_code":"US","type":"education","lineage":["https://openalex.org/I188538660"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kathryn Conger","raw_affiliation_strings":["University of Colorado Boulder"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Colorado Boulder","institution_ids":["https://openalex.org/I188538660"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017464054","display_name":"Martha Stone Palmer","orcid":"https://orcid.org/0000-0001-9864-6974","observed_orcids":["https://orcid.org/0000-0001-9864-6974"]},"institutions":[{"id":"https://openalex.org/I188538660","display_name":"University of Colorado Boulder","ror":"https://ror.org/02ttsq026","country_code":"US","type":"education","lineage":["https://openalex.org/I188538660"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Martha Palmer","raw_affiliation_strings":["University of Colorado Boulder"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Colorado Boulder","institution_ids":["https://openalex.org/I188538660"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103178893","display_name":"Heng Ji","orcid":"https://orcid.org/0000-0002-7954-7994","observed_orcids":["https://orcid.org/0000-0002-7954-7994"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heng Ji","raw_affiliation_strings":["University of Illinois Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019539533","display_name":"Jiawei Han","orcid":"https://orcid.org/0000-0002-3629-2696","observed_orcids":["https://orcid.org/0000-0002-3629-2696"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiawei Han","raw_affiliation_strings":["University of Illinois Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.068,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.93796969,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2823","last_page":"2838"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9672999978065491,"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":0.9672999978065491,"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":0.019700000062584877,"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":0.0032999999821186066,"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/event-extraction","display_name":"event extraction","score":0.8799999952316284},{"id":"https://openalex.org/keywords/glens","display_name":"glens","score":0.7200000286102295},{"id":"https://openalex.org/keywords/propbank","display_name":"PropBank","score":0.6399999856948853},{"id":"https://openalex.org/keywords/wikidata","display_name":"Wikidata","score":0.746999979019165},{"id":"https://openalex.org/keywords/remote-supervision","display_name":"distant supervision","score":0.8939999938011169},{"id":"https://openalex.org/keywords/ontology-coverage","display_name":"ontology coverage","score":0.47099998593330383},{"id":"https://openalex.org/keywords/noisy-labels","display_name":"label noise","score":0.781000018119812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8541131019592285},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.7775228023529053},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6819810271263123},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.6255362033843994},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4380878210067749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43793830275535583},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.43537187576293945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42697712779045105},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3944704234600067},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3512873947620392},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07460269331932068},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.06886854767799377},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2023.emnlp-main.170","doi":"https://doi.org/10.18653/v1/2023.emnlp-main.170","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.emnlp-main.170","pdf_url":"https://aclanthology.org/2023.emnlp-main.170.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 2023 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2303.09093","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.09093","pdf_url":"https://arxiv.org/pdf/2303.09093","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2303.09093","doi":"https://doi.org/10.48550/arxiv.2303.09093","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2303.09093","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":"doi:10.18653/v1/2023.emnlp-main.170","doi":"https://doi.org/10.18653/v1/2023.emnlp-main.170","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.emnlp-main.170","pdf_url":"https://aclanthology.org/2023.emnlp-main.170.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 2023 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1472423617","display_name":null,"funder_award_id":"FA8750-19-2-1004","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G4713059963","display_name":null,"funder_award_id":"FA8750","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389519411.pdf","grobid_xml":"https://content.openalex.org/works/W4389519411.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W2115792525","https://openalex.org/W2158847908","https://openalex.org/W2250999640","https://openalex.org/W2251628379","https://openalex.org/W2618285232","https://openalex.org/W2803884531","https://openalex.org/W2830607191","https://openalex.org/W2946760275","https://openalex.org/W2952437275","https://openalex.org/W2963473975","https://openalex.org/W2970684294","https://openalex.org/W2981647022","https://openalex.org/W2983386625","https://openalex.org/W3034439313","https://openalex.org/W3035000929","https://openalex.org/W3035067238","https://openalex.org/W3035229828","https://openalex.org/W3106098584","https://openalex.org/W3170759063","https://openalex.org/W3174870841","https://openalex.org/W4226278401","https://openalex.org/W4287887158","https://openalex.org/W4385573480"],"related_works":["https://openalex.org/W7155571277","https://openalex.org/W3021222499","https://openalex.org/W4416035227","https://openalex.org/W3199386065","https://openalex.org/W6906584158","https://openalex.org/W4401042240","https://openalex.org/W4360978878","https://openalex.org/W3164941331","https://openalex.org/W4409747196","https://openalex.org/W4287887318"],"abstract_inverted_index":{"The":[0],"progress":[1],"of":[2,12,114],"event":[3,17,26,33,50,89],"extraction":[4,18],"research":[5],"has":[6],"been":[7],"hindered":[8],"by":[9,54],"the":[10,56,73,97,129],"absence":[11],"wide-coverage,":[13],"large-scale":[14],"datasets.To":[15],"make":[16],"systems":[19],"more":[20,41],"accessible,":[21],"we":[22,83,118],"build":[23],"a":[24,61,86,112],"generalpurpose":[25],"detection":[27,90],"dataset":[28],"GLEN":[29],"which":[30,59],"covers":[31],"205K":[32],"mentions":[34],"with":[35],"3,465":[36],"different":[37],"types,":[38],"making":[39],"it":[40],"than":[42,47],"20x":[43],"larger":[44],"in":[45,101],"ontology":[46,99],"today's":[48],"largest":[49,130],"dataset.GLEN":[51],"is":[52,127],"created":[53],"utilizing":[55],"DWD":[57],"Overlay,":[58],"provides":[60],"mapping":[62],"between":[63],"Wikidata":[64],"Qnodes":[65],"and":[66,122],"PropBank":[67,78],"rolesets.This":[68],"enables":[69],"us":[70],"to":[71,95,111],"use":[72],"abundant":[74],"existing":[75],"annotation":[76],"for":[77,132,135],"as":[79],"distant":[80],"supervision.In":[81],"addition,":[82],"also":[84],"propose":[85],"new":[87,137],"multi-stage":[88],"model":[91,106],"CEDAR":[92],"specifically":[93],"designed":[94],"handle":[96],"large":[98],"size":[100],"GLEN.We":[102],"show":[103,123],"that":[104,124],"our":[105],"exhibits":[107],"superior":[108],"performance":[109,134],"compared":[110],"range":[113],"baselines":[115],"including":[116],"InstructGPT.Finally,":[117],"perform":[119],"error":[120],"analysis":[121],"label":[125],"noise":[126],"still":[128],"challenge":[131],"improving":[133],"this":[136],"dataset.":[138],"1":[139]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2023-12-10T00:00:00"}