{"id":"https://openalex.org/W774598698","doi":"https://doi.org/10.18653/v1/k15-1016","title":"Instance Selection Improves Cross-Lingual Model Training for Fine-Grained Sentiment Analysis","display_name":"Instance Selection Improves Cross-Lingual Model Training for Fine-Grained Sentiment Analysis","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W774598698","doi":"https://doi.org/10.18653/v1/k15-1016","mag":"774598698"},"language":"en","primary_location":{"id":"doi:10.18653/v1/k15-1016","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k15-1016","pdf_url":"https://www.aclweb.org/anthology/K15-1016.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth Conference on Computational Natural Language Learning","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/K15-1016.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090743286","display_name":"Roman Klinger","orcid":"https://orcid.org/0000-0002-2014-6619"},"institutions":[{"id":"https://openalex.org/I100066346","display_name":"University of Stuttgart","ror":"https://ror.org/04vnq7t77","country_code":"DE","type":"education","lineage":["https://openalex.org/I100066346"]},{"id":"https://openalex.org/I20121455","display_name":"Bielefeld University","ror":"https://ror.org/02hpadn98","country_code":"DE","type":"education","lineage":["https://openalex.org/I20121455"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Roman Klinger","raw_affiliation_strings":["Institute for Natural Language Processing University of Stuttgart 70569 Stuttgart, Germany","Semantic Computing Group, CIT-EC Bielefeld University 33615 Bielefeld, Germany","University of stuttgart"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Natural Language Processing University of Stuttgart 70569 Stuttgart, Germany","institution_ids":["https://openalex.org/I100066346"]},{"raw_affiliation_string":"Semantic Computing Group, CIT-EC Bielefeld University 33615 Bielefeld, Germany","institution_ids":["https://openalex.org/I20121455"]},{"raw_affiliation_string":"University of stuttgart","institution_ids":["https://openalex.org/I100066346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056086147","display_name":"Philipp Cimiano","orcid":"https://orcid.org/0000-0002-4771-441X"},"institutions":[{"id":"https://openalex.org/I20121455","display_name":"Bielefeld University","ror":"https://ror.org/02hpadn98","country_code":"DE","type":"education","lineage":["https://openalex.org/I20121455"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Philipp Cimiano","raw_affiliation_strings":["Semantic Computing Group, CIT-EC Bielefeld University 33615 Bielefeld, Germany","Bielefeld University,;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Semantic Computing Group, CIT-EC Bielefeld University 33615 Bielefeld, Germany","institution_ids":["https://openalex.org/I20121455"]},{"raw_affiliation_string":"Bielefeld University,;","institution_ids":["https://openalex.org/I20121455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4102,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":{"value":0.54583199,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"153","last_page":"163"},"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/T10028","display_name":"Topic Modeling","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/T10181","display_name":"Natural Language Processing Techniques","score":0.9990000128746033,"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":0.8816088438034058},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.694549560546875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6886657476425171},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6398798227310181},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6232863664627075},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5817143321037292},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.550417423248291},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5497496724128723},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5490739941596985},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5318142175674438},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5114161372184753},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.49858570098876953},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4711177349090576}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8816088438034058},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.694549560546875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6886657476425171},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6398798227310181},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6232863664627075},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5817143321037292},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.550417423248291},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5497496724128723},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5490739941596985},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5318142175674438},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5114161372184753},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.49858570098876953},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4711177349090576},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"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/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C105580179","wikidata":"https://www.wikidata.org/wiki/Q188928","display_name":"Messenger RNA","level":3,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/k15-1016","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k15-1016","pdf_url":"https://www.aclweb.org/anthology/K15-1016.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth Conference on Computational Natural Language Learning","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.722.6300","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.722.6300","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.romanklinger.de/publications/klinger-conll2015.pdf","raw_type":"text"},{"id":"pmh:oai:pub.librecat.org:2759733","is_oa":false,"landing_page_url":"https://pub.uni-bielefeld.de/record/2759733","pdf_url":null,"source":{"id":"https://openalex.org/S4306401671","display_name":"PUB \u2013 Publications at Bielefeld University (Bielefeld University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20121455","host_organization_name":"Bielefeld University","host_organization_lineage":["https://openalex.org/I20121455"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Klinger R, Cimiano P. Instance selection improves cross-lingual model training for fine-grained sentiment analysis. In: <em>Proceedings of the Nineteenth Conference on Computational Natural Language Learning (CoNLL)</em>. Beijing, China; In Press.","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.18653/v1/k15-1016","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k15-1016","pdf_url":"https://www.aclweb.org/anthology/K15-1016.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth Conference on Computational Natural Language Learning","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5600000023841858}],"awards":[{"id":"https://openalex.org/G1927681210","display_name":null,"funder_award_id":"(EXC 277)","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G3429453619","display_name":null,"funder_award_id":"EXC 277","funder_id":"https://openalex.org/F4320323389","funder_display_name":"Universit\u00e4t Bielefeld"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320323389","display_name":"Universit\u00e4t Bielefeld","ror":"https://ror.org/02hpadn98"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W774598698.pdf","grobid_xml":"https://content.openalex.org/works/W774598698.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W8407316","https://openalex.org/W22168010","https://openalex.org/W70082214","https://openalex.org/W126222424","https://openalex.org/W181190445","https://openalex.org/W225641137","https://openalex.org/W1512175247","https://openalex.org/W1517771839","https://openalex.org/W1536516100","https://openalex.org/W1549434858","https://openalex.org/W1579082069","https://openalex.org/W1603678606","https://openalex.org/W1631260214","https://openalex.org/W1675450783","https://openalex.org/W1681829378","https://openalex.org/W1905522558","https://openalex.org/W1916559533","https://openalex.org/W1981256868","https://openalex.org/W1992042274","https://openalex.org/W2007148919","https://openalex.org/W2016630033","https://openalex.org/W2017489100","https://openalex.org/W2033712093","https://openalex.org/W2079442239","https://openalex.org/W2081375810","https://openalex.org/W2084499688","https://openalex.org/W2097831367","https://openalex.org/W2108765529","https://openalex.org/W2111362445","https://openalex.org/W2115057736","https://openalex.org/W2126530744","https://openalex.org/W2126704587","https://openalex.org/W2128278152","https://openalex.org/W2130262843","https://openalex.org/W2131211273","https://openalex.org/W2142262074","https://openalex.org/W2143954309","https://openalex.org/W2144571677","https://openalex.org/W2148259701","https://openalex.org/W2148708890","https://openalex.org/W2148922863","https://openalex.org/W2152691628","https://openalex.org/W2157076169","https://openalex.org/W2158188757","https://openalex.org/W2160660844","https://openalex.org/W2164076936","https://openalex.org/W2164984707","https://openalex.org/W2167660864","https://openalex.org/W2170694982","https://openalex.org/W2189271176","https://openalex.org/W2250785313","https://openalex.org/W2251902771","https://openalex.org/W2252221254","https://openalex.org/W2397907761","https://openalex.org/W3044695148","https://openalex.org/W3098663719","https://openalex.org/W3211848854","https://openalex.org/W4248506559","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138","https://openalex.org/W2743976221"],"abstract_inverted_index":{"Scarcity":[0],"of":[1,65,98,123,152],"annotated":[2,39,101],"corpora":[3],"for":[4,10,34,86,102,133],"many":[5],"languages":[6,104],"is":[7,155],"a":[8,42,46,51,62,70,96],"bottleneck":[9],"training":[11,35,159],"finegrained":[12],"sentiment":[13,54],"analysis":[14,55],"models":[15],"that":[16,50,149],"can":[17,57],"tag":[18],"aspects":[19],"and":[20,105],"subjective":[21,153],"phrases.":[22],"We":[23,88],"propose":[24,69],"to":[25,30,45,80,107,115,128,140,157],"exploit":[26],"statistical":[27],"machine":[28,75],"translation":[29,76],"alleviate":[31],"the":[32,91,121,150],"need":[33],"data":[36,40],"by":[37],"projecting":[38],"in":[41,120],"source":[43],"language":[44,48,92],"target":[47],"such":[49],"supervised":[52],"fine-grained":[53],"system":[56],"be":[58],"trained.":[59],"To":[60],"avoid":[61],"negative":[63],"influence":[64],"poor-quality":[66],"translations,":[67],"we":[68,147],"filtering":[71,113],"approach":[72,137],"based":[73],"on":[74,90,95],"quality":[77],"estimation":[78],"measures":[79],"select":[81],"only":[82],"high-quality":[83],"sentence":[84],"pairs":[85],"projection.":[87],"evaluate":[89],"pair":[93],"German/English":[94],"corpus":[97],"product":[99],"reviews":[100],"both":[103],"compare":[106],"in-target-language":[108,134,158],"training.":[109,135],"Projection":[110],"without":[111,160],"any":[112],"leads":[114],"23":[116],"%":[117,130,142],"F":[118,131,143],"1":[119,132,144],"task":[122],"detecting":[124],"aspect":[125],"phrases,":[126],"compared":[127],"41":[129],"Our":[136],"obtains":[138],"up":[139],"47":[141],".":[145],"Further,":[146],"show":[148],"detection":[151],"phrases":[154],"competitive":[156],"filtering.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":5},{"year":2016,"cited_by_count":2}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}