{"id":"https://openalex.org/W4327521036","doi":"https://doi.org/10.1145/3573428.3573749","title":"Heterogeneous Graph Neural Network for Chinese Financial Event Extraction","display_name":"Heterogeneous Graph Neural Network for Chinese Financial Event Extraction","publication_year":2022,"publication_date":"2022-10-21","ids":{"openalex":"https://openalex.org/W4327521036","doi":"https://doi.org/10.1145/3573428.3573749"},"language":"en","primary_location":{"id":"doi:10.1145/3573428.3573749","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573428.3573749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000577985","display_name":"Shunyu Yao","orcid":"https://orcid.org/0000-0002-1683-286X"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shunyu Yao","raw_affiliation_strings":["Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China"],"affiliations":[{"raw_affiliation_string":"Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066864248","display_name":"Jie Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Hu","raw_affiliation_strings":["Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China"],"affiliations":[{"raw_affiliation_string":"Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057734833","display_name":"Chuxiong Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuxiong Sun","raw_affiliation_strings":["Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China"],"affiliations":[{"raw_affiliation_string":"Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033394913","display_name":"Zhiqiao Gao","orcid":"https://orcid.org/0000-0002-6254-3655"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiao Gao","raw_affiliation_strings":["Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China"],"affiliations":[{"raw_affiliation_string":"Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740596","display_name":"Ning Liu","orcid":"https://orcid.org/0000-0002-3408-8632"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Liu","raw_affiliation_strings":["Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China"],"affiliations":[{"raw_affiliation_string":"Big Data and Artificial Intelligence Institute, China Telecom Research Institute, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5000577985"],"corresponding_institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"],"apc_list":null,"apc_paid":null,"fwci":0.1379,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57815384,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1822","last_page":"1827"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9983999729156494,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9983999729156494,"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":0.9976000189781189,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7532292604446411},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6694141626358032},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6169189214706421},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6000835299491882},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5650414228439331},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5299789905548096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49863290786743164},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4838625192642212},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33467864990234375},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.13791492581367493}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7532292604446411},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6694141626358032},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6169189214706421},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6000835299491882},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5650414228439331},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5299789905548096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49863290786743164},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4838625192642212},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33467864990234375},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.13791492581367493},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3573428.3573749","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573428.3573749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1528012351","https://openalex.org/W1607035479","https://openalex.org/W1940872118","https://openalex.org/W2064675550","https://openalex.org/W2072628044","https://openalex.org/W2108743083","https://openalex.org/W2130714105","https://openalex.org/W2147880316","https://openalex.org/W2164949130","https://openalex.org/W2250575108","https://openalex.org/W2250999640","https://openalex.org/W2508618307","https://openalex.org/W2519887557","https://openalex.org/W2562564313","https://openalex.org/W2733628661","https://openalex.org/W2747329762","https://openalex.org/W2788525741","https://openalex.org/W2803884531","https://openalex.org/W2896457183","https://openalex.org/W2952437275","https://openalex.org/W2964206023","https://openalex.org/W2970684294","https://openalex.org/W3011802752","https://openalex.org/W3104597568","https://openalex.org/W6681794421"],"related_works":["https://openalex.org/W2375873920","https://openalex.org/W2146114872","https://openalex.org/W2392060890","https://openalex.org/W2392760275","https://openalex.org/W2083530853","https://openalex.org/W2982905616","https://openalex.org/W2009831055","https://openalex.org/W2393172683","https://openalex.org/W2368686738","https://openalex.org/W4385572368"],"abstract_inverted_index":{"Financial":[0],"event":[1,13,30,69,74],"extraction":[2,70],"aims":[3],"to":[4,60,93,119],"detect":[5],"events":[6,42],"from":[7],"financial":[8,20,99],"announcements":[9,21],"and":[10,40,57,89,125,132],"extract":[11,73],"corresponding":[12],"arguments.":[14],"This":[15],"task":[16],"is":[17,91],"challenging":[18],"because":[19],"are":[22,31,82],"often":[23],"long":[24],"text,":[25],"the":[26,38,46,55,58,77,121,136],"arguments":[27,62,75],"of":[28,54,98,116,138],"an":[29],"always":[32],"scattered":[33],"among":[34],"different":[35],"sentences":[36,124],"in":[37,45],"document,":[39],"multiple":[41,64],"can":[43],"coexist":[44],"same":[47],"document.":[48],"It":[49],"requires":[50],"a":[51,95,107],"comprehensive":[52,133],"understanding":[53],"document":[56],"ability":[59],"aggregate":[61],"across":[63],"sentences.":[65],"Most":[66],"existing":[67],"sentence-level":[68],"methods":[71,81],"only":[72],"within":[76],"sentence":[78],"range.":[79],"These":[80],"not":[83],"very":[84],"effective":[85],"for":[86],"this":[87],"task,":[88],"it":[90],"difficult":[92],"handle":[94],"large":[96],"number":[97],"announcements.":[100],"To":[101],"address":[102],"these":[103],"issues,":[104],"we":[105],"propose":[106],"novel":[108],"heterogeneous":[109,128],"graph-based":[110],"model":[111],"HGCFEE":[112,139],"with":[113],"six":[114],"types":[115],"edges":[117],"designed":[118],"capture":[120],"interactions":[122],"between":[123],"entities":[126],"using":[127],"graphs.":[129],"In-depth":[130],"experiments":[131],"analysis":[134],"demonstrate":[135],"superiority":[137],"over":[140],"baseline":[141],"methods.":[142]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
