{"id":"https://openalex.org/W4313175264","doi":"https://doi.org/10.1109/access.2022.3228835","title":"Gated Relational Encoder-Decoder Model for Target-Oriented Opinion Word Extraction","display_name":"Gated Relational Encoder-Decoder Model for Target-Oriented Opinion Word Extraction","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4313175264","doi":"https://doi.org/10.1109/access.2022.3228835"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3228835","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3228835","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09982601.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09982601.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082023846","display_name":"Taegwan Kang","orcid":"https://orcid.org/0000-0002-9171-357X"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Taegwan Kang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-9171-357X","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026151562","display_name":"Segwang Kim","orcid":"https://orcid.org/0000-0003-2678-9858"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Segwang Kim","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-2678-9858","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044469790","display_name":"Hyeongu Yun","orcid":"https://orcid.org/0000-0002-1506-6200"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyeongu Yun","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-1506-6200","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063029769","display_name":"Hwanhee Lee","orcid":"https://orcid.org/0000-0002-9367-9811"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hwanhee Lee","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-9367-9811","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077832834","display_name":"Kyomin Jung","orcid":"https://orcid.org/0000-0003-2547-7051"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyomin Jung","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea","Electrical and Computer Engineering, Seoul National University, Seoul, Korea"],"raw_orcid":"https://orcid.org/0000-0003-2547-7051","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"Electrical and Computer Engineering, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5082023846"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.4166,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69602351,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"10","issue":null,"first_page":"130507","last_page":"130517"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","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/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":0.9972000122070312,"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":0.8186525106430054},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6238324046134949},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.557960569858551},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.47926875948905945},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4439212679862976},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.42548051476478577},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34510117769241333},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08908575773239136}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8186525106430054},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6238324046134949},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.557960569858551},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47926875948905945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4439212679862976},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.42548051476478577},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34510117769241333},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08908575773239136},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3228835","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3228835","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09982601.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:da89792559514463baec34950d519a01","is_oa":false,"landing_page_url":"https://doaj.org/article/da89792559514463baec34950d519a01","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 130507-130517 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3228835","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3228835","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09982601.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1763178188","display_name":null,"funder_award_id":"2021R1A2C2008855","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321292","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4313175264.pdf","grobid_xml":"https://content.openalex.org/works/W4313175264.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W113522833","https://openalex.org/W115678215","https://openalex.org/W1623072288","https://openalex.org/W1991133427","https://openalex.org/W2104731083","https://openalex.org/W2112744748","https://openalex.org/W2160660844","https://openalex.org/W2251294039","https://openalex.org/W2251648804","https://openalex.org/W2427312199","https://openalex.org/W2465978385","https://openalex.org/W2514722822","https://openalex.org/W2604205681","https://openalex.org/W2756816896","https://openalex.org/W2798405286","https://openalex.org/W2896457183","https://openalex.org/W2946015932","https://openalex.org/W2962741379","https://openalex.org/W2962843214","https://openalex.org/W2963240575","https://openalex.org/W2963556938","https://openalex.org/W2964105864","https://openalex.org/W2997194369","https://openalex.org/W2997464781","https://openalex.org/W2998446468","https://openalex.org/W3034999214","https://openalex.org/W3035407080","https://openalex.org/W3104579227","https://openalex.org/W3153479395","https://openalex.org/W3167172669","https://openalex.org/W3176038554","https://openalex.org/W3196474414","https://openalex.org/W3210083203","https://openalex.org/W4211186029","https://openalex.org/W4213458364","https://openalex.org/W4220758122","https://openalex.org/W4224016461","https://openalex.org/W4230515038","https://openalex.org/W4285601004","https://openalex.org/W4287824654","https://openalex.org/W4288089799","https://openalex.org/W6605538935","https://openalex.org/W6623774220","https://openalex.org/W6626820544","https://openalex.org/W6679076926","https://openalex.org/W6712608028","https://openalex.org/W6739901393","https://openalex.org/W6769311223","https://openalex.org/W6769627184","https://openalex.org/W6771917389","https://openalex.org/W6778883912"],"related_works":["https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W2142795561","https://openalex.org/W2053286651","https://openalex.org/W4205302943","https://openalex.org/W2561132942","https://openalex.org/W2296205523"],"abstract_inverted_index":{"Target-Oriented":[0],"Opinion":[1],"Word":[2],"Extraction":[3],"(TOWE)":[4],"is":[5,33],"a":[6,93,154,172],"challenging":[7],"information":[8,113,144,221],"extraction":[9],"task":[10],"that":[11,199,216],"aims":[12],"to":[13,21,35,157,186],"find":[14],"the":[15,37,48,60,74,112,115,121,125,129,136,141,146,160,163,166,182,188,203,220,223,229,234],"<italic":[16,23,41,49,61,83,116,130,224,235],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[17,24,42,50,62,84,117,131,225,236],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">opinion":[18,25,43,51,63,85,118,132,226,237],"words</i>":[19,44,86,238],"corresponding":[20],"given":[22],"targets</i>":[26,52],"in":[27],"text.":[28],"To":[29],"solve":[30],"TOWE,":[31,99],"it":[32],"important":[34],"consider":[36],"surrounding":[38,80,149],"words":[39,81],"of":[40,82,114,162,184,222],"as":[45,47,181],"well":[46],".":[53,87,239],"Although":[54],"most":[55],"existing":[56],"works":[57],"have":[58],"captured":[59],"target</i>":[64,119,133,227],"using":[65],"Deep":[66],"Neural":[67],"Networks":[68],"(DNNs),":[69],"they":[70],"cannot":[71],"effectively":[72],"utilize":[73],"local":[75,105,122,137,142,230],"context,":[76],"i.e.":[77],"relationship":[78,147],"among":[79,148],"In":[88,168],"this":[89],"work,":[90],"we":[91,170],"propose":[92],"novel":[94],"and":[95,104,120,135,165,177,206,228],"powerful":[96],"model":[97,175],"for":[98,232],"Gated":[100],"Relational":[101],"target-aware":[102,126],"Encoder":[103],"context-aware":[106,138],"Decoder":[107],"(GRED),":[108],"which":[109],"dynamically":[110,158],"leverages":[111,219],"context.":[123],"Intuitively,":[124],"encoder":[127,164],"catches":[128],"information,":[134],"decoder":[139],"obtains":[140],"context":[143,231],"from":[145],"words.":[150],"Then,":[151],"GRED":[152,185,200,217],"employs":[153],"gate":[155],"mechanism":[156],"aggregate":[159],"outputs":[161],"decoder.":[167],"addition,":[169],"adopt":[171],"pretrained":[173],"language":[174,190],"Bidirectional":[176],"Auto-Regressive":[178],"Transformer":[179],"(BART),":[180],"structure":[183],"improve":[187],"implicit":[189],"knowledge.":[191],"Extensive":[192],"experiments":[193],"on":[194],"four":[195],"benchmark":[196],"datasets":[197],"show":[198],"surpasses":[201],"all":[202],"baseline":[204],"models":[205],"achieves":[207],"state-of-the-":[208],"art":[209],"performance.":[210],"Furthermore,":[211],"our":[212],"in-depth":[213],"analysis":[214],"demonstrates":[215],"properly":[218],"extracting":[233]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
