{"id":"https://openalex.org/W3153870750","doi":"https://doi.org/10.1145/3404835.3463054","title":"Graph Learning Regularization and Transfer Learning for Few-Shot Event Detection","display_name":"Graph Learning Regularization and Transfer Learning for Few-Shot Event Detection","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3153870750","doi":"https://doi.org/10.1145/3404835.3463054","mag":"3153870750"},"language":"en","primary_location":{"id":"doi:10.1145/3404835.3463054","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3404835.3463054","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3404835.3463054","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3404835.3463054","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070047759","display_name":"Viet Dac Lai","orcid":"https://orcid.org/0009-0008-1651-4619"},"institutions":[{"id":"https://openalex.org/I181233156","display_name":"University of Oregon","ror":"https://ror.org/0293rh119","country_code":"US","type":"education","lineage":["https://openalex.org/I181233156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Viet Dac Lai","raw_affiliation_strings":["University of Oregon, EUGENE, OR, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oregon, EUGENE, OR, USA","institution_ids":["https://openalex.org/I181233156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101747540","display_name":"Minh Nguyen","orcid":"https://orcid.org/0000-0002-0811-6441"},"institutions":[{"id":"https://openalex.org/I181233156","display_name":"University of Oregon","ror":"https://ror.org/0293rh119","country_code":"US","type":"education","lineage":["https://openalex.org/I181233156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minh Van Nguyen","raw_affiliation_strings":["University of Oregon, EUGENE, OR, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oregon, EUGENE, OR, USA","institution_ids":["https://openalex.org/I181233156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026113034","display_name":"Thien Huu Nguyen","orcid":"https://orcid.org/0000-0003-3768-4736"},"institutions":[{"id":"https://openalex.org/I181233156","display_name":"University of Oregon","ror":"https://ror.org/0293rh119","country_code":"US","type":"education","lineage":["https://openalex.org/I181233156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thien Huu Nguyen","raw_affiliation_strings":["University of Oregon, EUGENE, OR, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oregon, EUGENE, OR, USA","institution_ids":["https://openalex.org/I181233156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028863551","display_name":"Franck Dernoncourt","orcid":"https://orcid.org/0000-0002-1119-1346"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Franck Dernoncourt","raw_affiliation_strings":["Adobe Inc., San Jose, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Adobe Inc., San Jose, CA, USA","institution_ids":["https://openalex.org/I1306409833"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7813,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.92343675,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2172","last_page":"2176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9922999739646912,"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":0.9847000241279602,"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/computer-science","display_name":"Computer science","score":0.7788238525390625},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.768398642539978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6762581467628479},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6056817173957825},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6017050743103027},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5969960689544678},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5471838116645813},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4922315180301666},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.41539645195007324},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.15658339858055115},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0961918830871582}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7788238525390625},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.768398642539978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6762581467628479},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6056817173957825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6017050743103027},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5969960689544678},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5471838116645813},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4922315180301666},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.41539645195007324},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.15658339858055115},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0961918830871582},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3404835.3463054","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3404835.3463054","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3404835.3463054","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3404835.3463054","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3404835.3463054","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3404835.3463054","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"No poverty","score":0.699999988079071,"id":"https://metadata.un.org/sdg/1"}],"awards":[{"id":"https://openalex.org/G1432373144","display_name":null,"funder_award_id":"W911NF-21-1-","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G2372970330","display_name":null,"funder_award_id":"W911NF-21-1-0112","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G638997368","display_name":null,"funder_award_id":"2019-19051600006","funder_id":"https://openalex.org/F4320333051","funder_display_name":"Intelligence Advanced Research Projects Activity"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G8726923708","display_name":"Phase I IUCRC University of Oregon: Center for Big Learning","funder_award_id":"1747798","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"},{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"},{"id":"https://openalex.org/F4320312530","display_name":"Office of the Director of National Intelligence","ror":"https://ror.org/01v3fsc55"},{"id":"https://openalex.org/F4320333051","display_name":"Intelligence Advanced Research Projects Activity","ror":"https://ror.org/01v3fsc55"},{"id":"https://openalex.org/F4320337349","display_name":"NIH Office of the Director","ror":"https://ror.org/00fj8a872"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3153870750.pdf","grobid_xml":"https://content.openalex.org/works/W3153870750.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W2035717317","https://openalex.org/W2072628044","https://openalex.org/W2081580037","https://openalex.org/W2108743083","https://openalex.org/W2130714105","https://openalex.org/W2165516035","https://openalex.org/W2185615741","https://openalex.org/W2211728022","https://openalex.org/W2250575108","https://openalex.org/W2250999640","https://openalex.org/W2251251652","https://openalex.org/W2432717477","https://openalex.org/W2475245295","https://openalex.org/W2508618307","https://openalex.org/W2517038008","https://openalex.org/W2519887557","https://openalex.org/W2572530946","https://openalex.org/W2601450892","https://openalex.org/W2618285232","https://openalex.org/W2739918945","https://openalex.org/W2788474500","https://openalex.org/W2890776849","https://openalex.org/W2905471643","https://openalex.org/W2946760275","https://openalex.org/W2952437275","https://openalex.org/W2963070905","https://openalex.org/W2963341956","https://openalex.org/W2963360413","https://openalex.org/W2964105864","https://openalex.org/W2979714393","https://openalex.org/W2981647022","https://openalex.org/W2995322030","https://openalex.org/W3006156620","https://openalex.org/W3034900014","https://openalex.org/W3035000929","https://openalex.org/W3036976209","https://openalex.org/W3045538350","https://openalex.org/W3096626844","https://openalex.org/W3099910226","https://openalex.org/W3101701554","https://openalex.org/W3104502980","https://openalex.org/W3137968039"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W2371138613","https://openalex.org/W2440023763","https://openalex.org/W2962474440","https://openalex.org/W2908248196","https://openalex.org/W2951706337","https://openalex.org/W2560283428","https://openalex.org/W2515319207","https://openalex.org/W4308565060","https://openalex.org/W4224292393"],"abstract_inverted_index":{"We":[0,45],"address":[1],"the":[2,58,90,98],"poor":[3],"generalization":[4,40],"of":[5],"few-shot":[6,32,66,99],"learning":[7,15,33,60,67,100,103],"models":[8,34,68,96],"for":[9,35,61,69,82,105],"event":[10,43],"detection":[11],"(ED)":[12],"using":[13],"transfer":[14,24],"and":[16,101,108],"representation":[17,59],"regularization.":[18],"In":[19],"particular,":[20],"we":[21,64],"propose":[22,47],"to":[23,37,41,56,77],"knowledge":[25],"from":[26,53],"open-domain":[27],"word":[28],"sense":[29],"disambiguation":[30],"into":[31],"ED":[36,70,75],"improve":[38],"their":[39],"new":[42],"types.":[44],"also":[46],"a":[48,72],"novel":[49],"training":[50],"signal":[51],"derived":[52],"dependency":[54],"graphs":[55],"regularize":[57],"ED.":[62,106],"Moreover,":[63],"evaluate":[65],"with":[71],"large-scale":[73],"human-annotated":[74],"dataset":[76],"obtain":[78],"more":[79],"reliable":[80],"insights":[81],"this":[83],"problem.":[84],"Our":[85],"comprehensive":[86],"experiments":[87],"demonstrate":[88],"that":[89],"proposed":[91],"model":[92],"outperforms":[93],"state-of-the-art":[94],"baseline":[95],"in":[97],"supervised":[102],"settings":[104],"Code":[107],"data":[109],"splits":[110],"are":[111],"available":[112],"at":[113],"https://github.com/laiviet/ed-fsl.":[114]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}