{"id":"https://openalex.org/W3190460602","doi":"https://doi.org/10.1109/access.2021.3101867","title":"LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model","display_name":"LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3190460602","doi":"https://doi.org/10.1109/access.2021.3101867","mag":"3190460602"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3101867","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3101867","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09503416.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":null,"license_id":null,"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/ielx7/6287639/9312710/09503416.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053168102","display_name":"Heereen Shim","orcid":"https://orcid.org/0000-0003-3761-2310"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Heereen Shim","raw_affiliation_strings":["Department of Electrical Engineering (ESAT), eMedia Research Laboratory and STADIUS, KU Leuven, Leuven, Belgium","Philip Research, Eindhoven, AE, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering (ESAT), eMedia Research Laboratory and STADIUS, KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]},{"raw_affiliation_string":"Philip Research, Eindhoven, AE, The Netherlands","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079803500","display_name":"Dietwig Lowet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dietwig Lowet","raw_affiliation_strings":["Philip Research, Eindhoven, AE, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Philip Research, Eindhoven, AE, The Netherlands","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018787960","display_name":"Stijn Luca","orcid":"https://orcid.org/0000-0002-6781-7870"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Stijn Luca","raw_affiliation_strings":["Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016823997","display_name":"Bart Vanrumste","orcid":"https://orcid.org/0000-0002-9409-935X"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Bart Vanrumste","raw_affiliation_strings":["Department of Electrical Engineering (ESAT), eMedia Research Laboratory and STADIUS, KU Leuven, Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering (ESAT), eMedia Research Laboratory and STADIUS, KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2075,"currency":"USD","value_usd":2075},"apc_paid":{"value":2075,"currency":"USD","value_usd":2075},"fwci":2.2868,"has_fulltext":true,"cited_by_count":24,"citation_normalized_percentile":{"value":0.89748191,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"9","issue":null,"first_page":"115563","last_page":"115578"},"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9994000196456909,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9988999962806702,"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":0.8619597554206848},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7595157623291016},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6970030069351196},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6657892465591431},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5799494981765747},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5728095769882202},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5545592904090881},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.553892970085144},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4561868906021118},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4463278651237488},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.42337584495544434},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41438430547714233}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8619597554206848},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7595157623291016},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6970030069351196},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6657892465591431},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5799494981765747},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5728095769882202},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5545592904090881},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.553892970085144},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4561868906021118},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4463278651237488},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.42337584495544434},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41438430547714233},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/access.2021.3101867","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3101867","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09503416.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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/678667","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/123456789/678667","pdf_url":"https://lirias.kuleuven.be/bitstream/123456789/678667/2/FINAL%20Article.pdf","source":{"id":"https://openalex.org/S7407055369","display_name":"Lirias","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. 9, (115563-115578)","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:archive.ugent.be:8717226","is_oa":true,"landing_page_url":"http://hdl.handle.net/1854/LU-8717226","pdf_url":null,"source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE ACCESS","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:1add3d7c4013401bb75ccbd12556a993","is_oa":true,"landing_page_url":"https://doaj.org/article/1add3d7c4013401bb75ccbd12556a993","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 115563-115578 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3101867","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3101867","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09503416.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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5799999833106995}],"awards":[{"id":"https://openalex.org/G434337789","display_name":null,"funder_award_id":"HORIZON2020","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G6102844168","display_name":"HEalth related Activity Recognition system based on IoT \u2013 an interdisciplinary training program for young researchers","funder_award_id":"766139","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320334679","display_name":"Research Executive Agency","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3190460602.pdf","grobid_xml":"https://content.openalex.org/works/W3190460602.grobid-xml"},"referenced_works_count":60,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1513874326","https://openalex.org/W1872312298","https://openalex.org/W1995875735","https://openalex.org/W2039643849","https://openalex.org/W2042932437","https://openalex.org/W2081580037","https://openalex.org/W2085989833","https://openalex.org/W2095705004","https://openalex.org/W2099550922","https://openalex.org/W2128518360","https://openalex.org/W2135815793","https://openalex.org/W2187089797","https://openalex.org/W2251648804","https://openalex.org/W2471138382","https://openalex.org/W2561529111","https://openalex.org/W2562607067","https://openalex.org/W2597787948","https://openalex.org/W2785787385","https://openalex.org/W2798820905","https://openalex.org/W2895547478","https://openalex.org/W2896457183","https://openalex.org/W2900401454","https://openalex.org/W2903158431","https://openalex.org/W2923978210","https://openalex.org/W2925618549","https://openalex.org/W2950899690","https://openalex.org/W2951786554","https://openalex.org/W2951911250","https://openalex.org/W2952357537","https://openalex.org/W2954278700","https://openalex.org/W2962808042","https://openalex.org/W2963238274","https://openalex.org/W2963341956","https://openalex.org/W2964059111","https://openalex.org/W2964207259","https://openalex.org/W2964282813","https://openalex.org/W2964288660","https://openalex.org/W2970597249","https://openalex.org/W2971296908","https://openalex.org/W2980708516","https://openalex.org/W3001060565","https://openalex.org/W3014249243","https://openalex.org/W3034238904","https://openalex.org/W3043005121","https://openalex.org/W3095830519","https://openalex.org/W3101345273","https://openalex.org/W3105522431","https://openalex.org/W6617145748","https://openalex.org/W6639393196","https://openalex.org/W6674330103","https://openalex.org/W6730042731","https://openalex.org/W6730529904","https://openalex.org/W6735374517","https://openalex.org/W6755207826","https://openalex.org/W6756615331","https://openalex.org/W6760568010","https://openalex.org/W6763701032","https://openalex.org/W6764456104","https://openalex.org/W6780996545"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W4382618745","https://openalex.org/W2885125400","https://openalex.org/W1001352512","https://openalex.org/W1989889224","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138"],"abstract_inverted_index":{"Recently":[0],"proposed":[1,52,117,187],"pre-trained":[2],"language":[3,199],"models":[4],"can":[5,136,168],"be":[6],"easily":[7],"fine-tuned":[8],"to":[9,61,70,81,91,109,144,170,177,181,196,220],"a":[10,17,27,39,92,120,226],"wide":[11],"range":[12],"of":[13,33,55,74,103,207,211,230],"downstream":[14],"tasks.":[15],"However,":[16],"large-scale":[18],"labelled":[19,75],"task-specific":[20,59,64,183],"dataset":[21,124],"is":[22],"required":[23],"for":[24,99],"fine-tuning":[25],"creating":[26],"bottleneck":[28],"in":[29],"the":[30,72,101,104,116,134,162,182,186,197,205,208,221],"development":[31,41],"process":[32],"machine":[34],"learning":[35,80,159],"applications.":[36],"To":[37],"foster":[38],"fast":[40],"by":[42,203,224],"reducing":[43],"manual":[44,138],"labelling":[45,139,145,213],"efforts,":[46],"we":[47,88],"propose":[48],"aLabel-EfficientTrainingScheme":[49],"(LETS).":[50],"The":[51,152],"LETS":[53,90,118,135,153,167],"consists":[54],"three":[56],"elements:":[57],"(i)":[58],"pre-training":[60,184],"exploit":[62],"unlabelled":[63],"corpus":[65],"data,":[66,76],"(ii)":[67],"label":[68,82,188],"augmentation":[69],"maximise":[71],"utility":[73],"and":[77,125,185],"(iii)":[78],"active":[79,158],"data":[83],"strategically.":[84],"In":[85],"this":[86,192],"paper,":[87],"apply":[89],"novel":[93],"aspect-based":[94],"sentiment":[95],"analysis":[96],"(ABSA)":[97],"use-case":[98],"analysing":[100],"reviews":[102],"health-related":[105,122],"program":[106],"supporting":[107],"people":[108],"improve":[110],"their":[111],"sleep":[112],"quality.":[113],"We":[114,190],"validate":[115],"on":[119,149],"custom":[121],"program-reviews":[123],"another":[126],"ABSA":[127],"benchmark":[128],"dataset.":[129],"Experimental":[130],"results":[131,164],"show":[132,165],"that":[133,166],"reduce":[137],"efforts":[140],"2-3":[141],"times":[142],"compared":[143,176],"with":[146,173],"random":[147],"sampling":[148],"both":[150,174],"datasets.":[151],"also":[154],"outperforms":[155],"other":[156,178],"state-of-the-art":[157],"methods.":[160],"Furthermore,":[161],"experimental":[163],"contribute":[169,195,219],"better":[171],"generalisability":[172],"datasets":[175],"methods":[179],"thanks":[180],"augmentation.":[189],"expect":[191],"work":[193,217],"could":[194,218],"natural":[198],"processing":[200],"(NLP)":[201],"domain":[202,223],"addressing":[204],"issue":[206],"high":[209],"cost":[210],"manually":[212],"data.":[214],"Also,":[215],"our":[216],"healthcare":[222],"introducing":[225],"new":[227],"potential":[228],"application":[229],"NLP":[231],"techniques.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":7}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
