{"id":"https://openalex.org/W3034473085","doi":"https://doi.org/10.24963/ijcai.2020/198","title":"Joint Representation Learning of Legislator and Legislation for Roll Call Prediction","display_name":"Joint Representation Learning of Legislator and Legislation for Roll Call Prediction","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3034473085","doi":"https://doi.org/10.24963/ijcai.2020/198","mag":"3034473085"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/198","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/198","pdf_url":"https://www.ijcai.org/proceedings/2020/0198.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2020/0198.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064388863","display_name":"Yuqiao Yang","orcid":"https://orcid.org/0009-0000-7761-3495"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]},{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN","JP"],"is_corresponding":false,"raw_author_name":"Yuqiao Yang","raw_affiliation_strings":["Fudan University, China","Tokyo Institute of Technology, Japan","School of Information and Communication, Tokyo Institute of Technology, Japan","School of Data Science, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]},{"raw_affiliation_string":"School of Information and Communication, Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]},{"raw_affiliation_string":"School of Data Science, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111731027","display_name":"Xiaoqiang Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqiang Lin","raw_affiliation_strings":["Fudan University, China","School of Data Science, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Data Science, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047306431","display_name":"Geng Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Geng Lin","raw_affiliation_strings":["Fudan University, China","School of Data Science, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Data Science, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062549536","display_name":"Zengfeng Huang","orcid":"https://orcid.org/0000-0003-2671-7483"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zengfeng Huang","raw_affiliation_strings":["Fudan University, China","School of Data Science, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Data Science, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028038982","display_name":"Changjian Jiang","orcid":"https://orcid.org/0000-0003-1774-1385"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]},{"id":"https://openalex.org/I67636235","display_name":"University of International Relations","ror":"https://ror.org/04r72en83","country_code":"CN","type":"education","lineage":["https://openalex.org/I67636235"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changjian Jiang","raw_affiliation_strings":["Fudan University, China","School of International Relations & Public Affairs, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of International Relations & Public Affairs, Fudan University, China","institution_ids":["https://openalex.org/I24943067","https://openalex.org/I67636235"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011504177","display_name":"Zhongyu Wei","orcid":"https://orcid.org/0000-0003-3789-8507"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongyu Wei","raw_affiliation_strings":["Fudan University, China","School of Data Science, Fudan University, China","Research Institute of Intelligent and Complex Systems, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Data Science, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Research Institute of Intelligent and Complex Systems, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1424","last_page":"1430"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9940999746322632,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9940999746322632,"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/T10028","display_name":"Topic Modeling","score":0.9728000164031982,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9495000243186951,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/legislator","display_name":"Legislator","score":0.9466730356216431},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7577544450759888},{"id":"https://openalex.org/keywords/legislation","display_name":"Legislation","score":0.7130383849143982},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5756404399871826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5573053956031799},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5354884266853333},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5062431693077087},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48479360342025757},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.479660302400589},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46373119950294495},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.343832790851593},{"id":"https://openalex.org/keywords/law","display_name":"Law","score":0.13229858875274658},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.12370556592941284}],"concepts":[{"id":"https://openalex.org/C2781287902","wikidata":"https://www.wikidata.org/wiki/Q4175034","display_name":"Legislator","level":3,"score":0.9466730356216431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577544450759888},{"id":"https://openalex.org/C2777351106","wikidata":"https://www.wikidata.org/wiki/Q49371","display_name":"Legislation","level":2,"score":0.7130383849143982},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5756404399871826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5573053956031799},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5354884266853333},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5062431693077087},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48479360342025757},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.479660302400589},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46373119950294495},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.343832790851593},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.13229858875274658},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.12370556592941284},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/198","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/198","pdf_url":"https://www.ijcai.org/proceedings/2020/0198.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/198","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/198","pdf_url":"https://www.ijcai.org/proceedings/2020/0198.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6299999952316284,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G3572139228","display_name":null,"funder_award_id":"18DZ1201000","funder_id":"https://openalex.org/F4320321885","funder_display_name":"Science and Technology Commission of Shanghai Municipality"},{"id":"https://openalex.org/G4918981142","display_name":null,"funder_award_id":"61751201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6142973028","display_name":null,"funder_award_id":"17JC1420200","funder_id":"https://openalex.org/F4320321885","funder_display_name":"Science and Technology Commission of Shanghai Municipality"},{"id":"https://openalex.org/G6453983564","display_name":null,"funder_award_id":"71991471","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6669166467","display_name":null,"funder_award_id":"No. 71991471","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"},{"id":"https://openalex.org/F4320321885","display_name":"Science and Technology Commission of Shanghai Municipality","ror":"https://ror.org/03kt66j61"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034473085.pdf","grobid_xml":"https://content.openalex.org/works/W3034473085.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1532499126","https://openalex.org/W1888005072","https://openalex.org/W1975517671","https://openalex.org/W2064675550","https://openalex.org/W2100799972","https://openalex.org/W2103841034","https://openalex.org/W2104290444","https://openalex.org/W2119082646","https://openalex.org/W2119290332","https://openalex.org/W2139823104","https://openalex.org/W2140354767","https://openalex.org/W2145767445","https://openalex.org/W2154851992","https://openalex.org/W2159291644","https://openalex.org/W2530041791","https://openalex.org/W2740934577","https://openalex.org/W2757026815","https://openalex.org/W2766224948","https://openalex.org/W2808313800","https://openalex.org/W2962756421","https://openalex.org/W2963099470","https://openalex.org/W2963571218","https://openalex.org/W2964015378","https://openalex.org/W2965889402","https://openalex.org/W2997701990","https://openalex.org/W3104097132","https://openalex.org/W4297571622","https://openalex.org/W4297845926"],"related_works":["https://openalex.org/W2364725704","https://openalex.org/W2276179314","https://openalex.org/W2372943929","https://openalex.org/W3180521552","https://openalex.org/W2495067141","https://openalex.org/W2329424900","https://openalex.org/W2943794226","https://openalex.org/W2210680337","https://openalex.org/W2331719217","https://openalex.org/W2602306023"],"abstract_inverted_index":{"In":[0,101],"this":[1,24],"paper,":[2],"we":[3,71,88,114],"explore":[4],"to":[5,56,60,103,141],"learn":[6,61],"representations":[7,63,108],"of":[8,15,41,49,107,132],"legislation":[9],"and":[10,67],"legislator":[11],"for":[12,23,38,64,82,98,119,135],"the":[13,28,46,50,110,120,130],"prediction":[14,139],"roll":[16,136],"call":[17,137],"results.":[18],"The":[19],"most":[20],"popular":[21],"approach":[22],"topic":[25],"is":[26],"named":[27],"ideal":[29],"point":[30],"model":[31,134],"that":[32],"relies":[33],"on":[34,125],"historical":[35],"voting":[36],"information":[37,48,59],"representation":[39,84,99],"learning":[40],"legislators.":[42],"It":[43],"largely":[44],"ignores":[45],"context":[47,58],"legislative":[51],"data.":[52],"We,":[53],"therefore,":[54],"propose":[55],"incorporate":[57,72],"dense":[62],"both":[65],"legislators":[66],"legislation.":[68],"For":[69,86],"legislators,":[70],"relations":[73],"among":[74],"them":[75],"via":[76,93],"graph":[77],"convolutional":[78],"neural":[79,95],"networks":[80,96],"(GCN)":[81],"their":[83],"learning.":[85,100],"legislation,":[87],"utilize":[89],"its":[90],"narrative":[91],"description":[92],"recurrent":[94],"(RNN)":[97],"order":[102],"align":[104],"two":[105],"kinds":[106],"in":[109],"same":[111],"vector":[112],"space,":[113],"introduce":[115],"a":[116,126],"triplet":[117],"loss":[118],"joint":[121],"training.":[122],"Experimental":[123],"results":[124,138],"self-constructed":[127],"dataset":[128],"show":[129],"effectiveness":[131],"our":[133],"compared":[140],"some":[142],"state-of-the-art":[143],"baselines.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}