{"id":"https://openalex.org/W2977753488","doi":"https://doi.org/10.1109/ijcnn.2019.8851873","title":"Selective Expression For Event Coreference Resolution on Twitter","display_name":"Selective Expression For Event Coreference Resolution on Twitter","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977753488","doi":"https://doi.org/10.1109/ijcnn.2019.8851873","mag":"2977753488"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5110573982","display_name":"Wenhan Chao","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenhan Chao","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113987537","display_name":"Wei Ping","orcid":"https://orcid.org/0000-0002-8852-6618"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Wei","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101037209","display_name":"Zhunchen Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210158522","display_name":"PLA Academy of Military Science","ror":"https://ror.org/05ct4s596","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210158522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhunchen Luo","raw_affiliation_strings":["Information Research Center of Military Science, PLA Academy of Military Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Information Research Center of Military Science, PLA Academy of Military Science, Beijing, China","institution_ids":["https://openalex.org/I4210158522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100441089","display_name":"Xiao Liu","orcid":"https://orcid.org/0000-0002-8893-366X"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Liu","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083265093","display_name":"Guobin Sui","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guobin Sui","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5110573982"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.5601,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.7536015,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9997000098228455,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9962000250816345,"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/coreference","display_name":"Coreference","score":0.9269455671310425},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8295470476150513},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.686951756477356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6366629004478455},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.602689266204834},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5581364631652832},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5282964706420898},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4326391816139221},{"id":"https://openalex.org/keywords/expression","display_name":"Expression (computer science)","score":0.43021583557128906},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4266626238822937},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.32626432180404663},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08137261867523193}],"concepts":[{"id":"https://openalex.org/C28076734","wikidata":"https://www.wikidata.org/wiki/Q63087","display_name":"Coreference","level":3,"score":0.9269455671310425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8295470476150513},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.686951756477356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6366629004478455},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.602689266204834},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5581364631652832},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5282964706420898},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4326391816139221},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.43021583557128906},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4266626238822937},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32626432180404663},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08137261867523193},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W191584165","https://openalex.org/W746041417","https://openalex.org/W2010581447","https://openalex.org/W2021836477","https://openalex.org/W2053154970","https://openalex.org/W2064675550","https://openalex.org/W2077428231","https://openalex.org/W2080527295","https://openalex.org/W2096335387","https://openalex.org/W2098345921","https://openalex.org/W2123661878","https://openalex.org/W2130942839","https://openalex.org/W2157944021","https://openalex.org/W2158266063","https://openalex.org/W2170471032","https://openalex.org/W2250335667","https://openalex.org/W2250487771","https://openalex.org/W2250521169","https://openalex.org/W2250575108","https://openalex.org/W2250668331","https://openalex.org/W2250999640","https://openalex.org/W2251110706","https://openalex.org/W2252089544","https://openalex.org/W2252094050","https://openalex.org/W2517784737","https://openalex.org/W2525778437","https://openalex.org/W2566266660","https://openalex.org/W2615915281","https://openalex.org/W2912242131","https://openalex.org/W2951527505","https://openalex.org/W2964121744","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W2139373276","https://openalex.org/W2227889443","https://openalex.org/W1509033667","https://openalex.org/W3041549465","https://openalex.org/W2380610138","https://openalex.org/W3171444480","https://openalex.org/W4206648670","https://openalex.org/W1495336436","https://openalex.org/W3212412177","https://openalex.org/W3173436410"],"abstract_inverted_index":{"With":[0],"the":[1,20,66,86,105,116,133,152,169,174,194,218],"growth":[2],"in":[3,27,89,217],"popularity":[4],"and":[5,49,63,108,148,162,200],"size":[6],"of":[7,41,58,68,119,136,151,203],"social":[8,30],"media,":[9],"there":[10],"is":[11,53],"an":[12,142],"urgent":[13],"need":[14],"for":[15],"systems":[16],"that":[17,192,211],"can":[18],"recognize":[19],"coreference":[21,35,171],"relation":[22],"between":[23,173,197],"two":[24,153],"event":[25,34,82,137,154,175,198,201,205],"mentions":[26,199],"texts":[28],"from":[29,45],"media.":[31],"In":[32,70],"existing":[33],"resolution":[36],"research,":[37],"a":[38,75,96,124,165,184],"rich":[39],"set":[40],"linguistic":[42],"features":[43,159],"derived":[44],"pre-existing":[46],"NLP":[47],"tools":[48],"various":[50],"knowledge":[51],"bases":[52],"often":[54],"required.":[55],"This":[56],"kind":[57],"methods":[59],"restricts":[60],"domain":[61],"scalability":[62],"leads":[64],"to":[65,84,103,113,131,167],"propagation":[67],"errors.":[69],"this":[71],"paper,":[72],"we":[73,94,122,140,182],"present":[74],"novel":[76],"selective":[77],"expression":[78],"approach":[79,213],"based":[80,145],"on":[81,126,146],"trigger":[83,202],"explore":[85],"coreferential":[87,195],"relationship":[88,172,196],"high-volume":[90],"Twitter":[91],"texts.":[92],"Firstly,":[93],"exploit":[95],"bidirectional":[97],"Long":[98],"Short":[99],"Term":[100],"Memory":[101],"(Bi-LSTM)":[102],"extract":[104],"sentence":[106,127],"level":[107,110,128],"mention":[109,138,176],"features.":[111,129],"Then,":[112],"selectively":[114],"express":[115],"essential":[117],"parts":[118],"generated":[120],"features,":[121],"apply":[123],"gate":[125],"Next,":[130],"integrate":[132],"time":[134,149],"information":[135],"pairs,":[139],"design":[141],"auxiliary":[143],"feature":[144],"triggers":[147],"attributes":[150],"mentions.":[155],"Finally,":[156],"all":[157],"these":[158],"are":[160],"concatenated":[161],"fed":[163],"into":[164],"classifier":[166],"predict":[168],"binary":[170],"pair.":[177],"To":[178],"evaluate":[179],"our":[180,212],"method,":[181],"publish":[183],"new":[185],"dataset":[186],"EventCoreOnTweet":[187],"(ECT)":[188],"<sup":[189],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[190],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[191],"annotates":[193],"each":[204],"mention.":[206],"The":[207],"experimental":[208],"results":[209],"demonstrate":[210],"achieves":[214],"significant":[215],"performance":[216],"ECT":[219],"dataset.":[220]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
