{"id":"https://openalex.org/W2891672860","doi":"https://doi.org/10.18653/v1/w18-5503","title":"Crowdsourcing Semantic Label Propagation in Relation Classification","display_name":"Crowdsourcing Semantic Label Propagation in Relation Classification","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2891672860","doi":"https://doi.org/10.18653/v1/w18-5503","mag":"2891672860"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w18-5503","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-5503","pdf_url":"https://www.aclweb.org/anthology/W18-5503.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 First Workshop on Fact Extraction and VERification (FEVER)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W18-5503.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021638892","display_name":"Anca Dumitrache","orcid":"https://orcid.org/0000-0003-1537-1763"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Anca Dumitrache","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016381010","display_name":"Lora Aroyo","orcid":"https://orcid.org/0000-0001-9402-1133"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lora Aroyo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5106085231","display_name":"Chris Welty","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chris Welty","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021638892"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.10596926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"16","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.996999979019165,"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":0.9872000217437744,"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/crowdsourcing","display_name":"Crowdsourcing","score":0.8500754833221436},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7404215335845947},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.7078081965446472},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.68921959400177},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.670884370803833},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6592026948928833},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.571003794670105},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5503114461898804},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5310459733009338},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4960545003414154},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.47534725069999695},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4674780070781708},{"id":"https://openalex.org/keywords/semantic-relation","display_name":"Semantic relation","score":0.43312519788742065},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.17825159430503845},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.09750670194625854}],"concepts":[{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.8500754833221436},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404215335845947},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.7078081965446472},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.68921959400177},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.670884370803833},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6592026948928833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.571003794670105},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5503114461898804},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5310459733009338},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4960545003414154},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.47534725069999695},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4674780070781708},{"id":"https://openalex.org/C2988080768","wikidata":"https://www.wikidata.org/wiki/Q7095057","display_name":"Semantic relation","level":3,"score":0.43312519788742065},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.17825159430503845},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.09750670194625854},{"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/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/w18-5503","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-5503","pdf_url":"https://www.aclweb.org/anthology/W18-5503.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 First Workshop on Fact Extraction and VERification (FEVER)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1809.00537","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.00537","pdf_url":"https://arxiv.org/pdf/1809.00537","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2891672860","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1809.00537.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1809.00537","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1809.00537","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.18653/v1/w18-5503","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-5503","pdf_url":"https://www.aclweb.org/anthology/W18-5503.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 First Workshop on Fact Extraction and VERification (FEVER)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4000000059604645,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2891672860.pdf","grobid_xml":"https://content.openalex.org/works/W2891672860.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1630959083","https://openalex.org/W1852412531","https://openalex.org/W1962580118","https://openalex.org/W2022881688","https://openalex.org/W2095603291","https://openalex.org/W2102267996","https://openalex.org/W2107598941","https://openalex.org/W2162130683","https://openalex.org/W2251622960","https://openalex.org/W2251832915","https://openalex.org/W2251847161","https://openalex.org/W2272061611","https://openalex.org/W2357843841","https://openalex.org/W2469104253","https://openalex.org/W2604610161","https://openalex.org/W2740759433","https://openalex.org/W2886159104","https://openalex.org/W2950133940","https://openalex.org/W2962881743","https://openalex.org/W3030068568"],"related_works":["https://openalex.org/W2963762253","https://openalex.org/W2971962903","https://openalex.org/W3156091046","https://openalex.org/W2901973642","https://openalex.org/W3155942419","https://openalex.org/W3159685758","https://openalex.org/W1525099456","https://openalex.org/W2979006783","https://openalex.org/W2944081799","https://openalex.org/W2964173876","https://openalex.org/W2604818337","https://openalex.org/W3021366976","https://openalex.org/W1988576742","https://openalex.org/W2255747889","https://openalex.org/W2912410189","https://openalex.org/W2799153978","https://openalex.org/W3114203687","https://openalex.org/W2962702189","https://openalex.org/W2149273804","https://openalex.org/W2906315171"],"abstract_inverted_index":{"Distant":[0],"supervision":[1],"is":[2,13,37],"a":[3,88,109],"popular":[4],"method":[5],"for":[6,59,67],"performing":[7],"relation":[8,22,61,112],"extraction":[9,23],"from":[10],"text":[11],"that":[12,39,69,84],"known":[14],"to":[15,32,82],"produce":[16],"noisy":[17],"labels.":[18],"Most":[19],"progress":[20],"in":[21,72,87,108],"and":[24,35],"classification":[25,62],"has":[26],"been":[27],"made":[28],"with":[29],"crowdsourced":[30],"corrections":[31],"distant-supervised":[33],"labels,":[34],"there":[36],"evidence":[38],"indicates":[40],"still":[41],"more":[42],"would":[43],"be":[44],"better.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49],"explore":[50],"the":[51,64,94],"problem":[52],"of":[53,96,101],"propagating":[54],"human":[55],"annotation":[56],"signals":[57],"gathered":[58],"open-domain":[60],"through":[63],"CrowdTruth":[65],"methodology":[66],"crowdsourcing,":[68],"captures":[70],"ambiguity":[71],"annotations":[73,81],"by":[74,98],"measuring":[75],"inter-annotator":[76],"disagreement.":[77],"Our":[78,103],"approach":[79],"propagates":[80],"sentences":[83],"are":[85],"similar":[86],"low":[89],"dimensional":[90],"embedding":[91],"space,":[92],"expanding":[93],"number":[95],"labels":[97],"two":[99],"orders":[100],"magnitude.":[102],"experiments":[104],"show":[105],"significant":[106],"improvement":[107],"sentencelevel":[110],"multi-class":[111],"classifier.":[113]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}