{"id":"https://openalex.org/W4289139443","doi":"https://doi.org/10.25080/majora-212e5952-001","title":"Semi-Supervised Semantic Annotator (S3A): Toward Efficient Semantic Labeling","display_name":"Semi-Supervised Semantic Annotator (S3A): Toward Efficient Semantic Labeling","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4289139443","doi":"https://doi.org/10.25080/majora-212e5952-001"},"language":"en","primary_location":{"id":"doi:10.25080/majora-212e5952-001","is_oa":true,"landing_page_url":"https://doi.org/10.25080/majora-212e5952-001","pdf_url":"http://conference.scipy.org/proceedings/scipy2022/pdfs/221_jessurun.pdf","source":{"id":"https://openalex.org/S4220651651","display_name":"Proceedings of the Python in Science Conferences","issn_l":"2575-9752","issn":["2575-9752"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"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 Python in Science Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"http://conference.scipy.org/proceedings/scipy2022/pdfs/221_jessurun.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080074107","display_name":"Nathan Jessurun","orcid":"https://orcid.org/0000-0003-1693-3641"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nathan Jessurun","raw_affiliation_strings":["University of Florida"],"affiliations":[{"raw_affiliation_string":"University of Florida","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034791927","display_name":"Daniel E. Capecci","orcid":"https://orcid.org/0000-0003-4494-5914"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Capecci","raw_affiliation_strings":["University of Florida"],"affiliations":[{"raw_affiliation_string":"University of Florida","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040726498","display_name":"Olivia P. Dizon-Paradis","orcid":"https://orcid.org/0000-0002-6879-8624"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Olivia Dizon-Paradis","raw_affiliation_strings":["University of Florida"],"affiliations":[{"raw_affiliation_string":"University of Florida","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055751228","display_name":"Damon L. Woodard","orcid":"https://orcid.org/0000-0002-0471-177X"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Damon Woodard","raw_affiliation_strings":["University of Florida"],"affiliations":[{"raw_affiliation_string":"University of Florida","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103242416","display_name":"Navid Asadizanjani","orcid":"https://orcid.org/0000-0003-3347-5072"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Navid Asadizanjani","raw_affiliation_strings":["University of Florida"],"affiliations":[{"raw_affiliation_string":"University of Florida","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5080074107"],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":0.3018,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.53514722,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"7","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9980999827384949,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9891999959945679,"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":0.8125150203704834},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.6255234479904175},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.6234353184700012},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5573239922523499},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5449709892272949},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5349570512771606},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.533481240272522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4671902656555176},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4626944959163666},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.4403042793273926},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.16579625010490417},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.15630552172660828}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8125150203704834},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.6255234479904175},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.6234353184700012},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5573239922523499},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5449709892272949},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5349570512771606},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.533481240272522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4671902656555176},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4626944959163666},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4403042793273926},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.16579625010490417},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.15630552172660828},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.25080/majora-212e5952-001","is_oa":true,"landing_page_url":"https://doi.org/10.25080/majora-212e5952-001","pdf_url":"http://conference.scipy.org/proceedings/scipy2022/pdfs/221_jessurun.pdf","source":{"id":"https://openalex.org/S4220651651","display_name":"Proceedings of the Python in Science Conferences","issn_l":"2575-9752","issn":["2575-9752"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"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 Python in Science Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.25080/majora-212e5952-001","is_oa":true,"landing_page_url":"https://doi.org/10.25080/majora-212e5952-001","pdf_url":"http://conference.scipy.org/proceedings/scipy2022/pdfs/221_jessurun.pdf","source":{"id":"https://openalex.org/S4220651651","display_name":"Proceedings of the Python in Science Conferences","issn_l":"2575-9752","issn":["2575-9752"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"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 Python in Science Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4289139443.pdf","grobid_xml":"https://content.openalex.org/works/W4289139443.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1537767494","https://openalex.org/W1610707153","https://openalex.org/W1861492603","https://openalex.org/W1908985308","https://openalex.org/W2019273017","https://openalex.org/W2019965591","https://openalex.org/W2031489346","https://openalex.org/W2036075471","https://openalex.org/W2056962391","https://openalex.org/W2068234316","https://openalex.org/W2082183672","https://openalex.org/W2103490241","https://openalex.org/W2127890285","https://openalex.org/W2131850886","https://openalex.org/W2155195832","https://openalex.org/W2156637418","https://openalex.org/W2171443096","https://openalex.org/W2396622801","https://openalex.org/W2577108375","https://openalex.org/W2793406320","https://openalex.org/W2943196448","https://openalex.org/W2963698633","https://openalex.org/W2964060775","https://openalex.org/W3089539777","https://openalex.org/W3109706338","https://openalex.org/W3117219596","https://openalex.org/W3126953616","https://openalex.org/W4288400169"],"related_works":["https://openalex.org/W1992807924","https://openalex.org/W2773959851","https://openalex.org/W2062427795","https://openalex.org/W1636283687","https://openalex.org/W2168699539","https://openalex.org/W2757541005","https://openalex.org/W2910550908","https://openalex.org/W4296302578","https://openalex.org/W3202806639","https://openalex.org/W2082060955"],"abstract_inverted_index":{"Most":[0],"semantic":[1,68],"image":[2,63],"annotation":[3],"platforms":[4],"suffer":[5],"severe":[6],"bottlenecks":[7],"when":[8],"handling":[9],"large":[10],"images,":[11],"complex":[12],"regions":[13,20],"of":[14,36,43,60,85,90],"interest,":[15],"or":[16],"numerous":[17],"distinct":[18],"foreground":[19],"in":[21],"a":[22,50,55],"single":[23],"image.":[24],"We":[25],"have":[26],"developed":[27],"the":[28,67,72,96],"Semi-Supervised":[29],"Semantic":[30],"Annotator":[31],"(S3A)":[32],"to":[33,103],"address":[34],"each":[35],"these":[37],"issues":[38],"and":[39,57,81,88,98],"facilitate":[40],"rapid":[41],"collection":[42],"ground":[44],"truth":[45],"pixel-level":[46],"labeled":[47],"data.":[48],"Such":[49],"feat":[51],"is":[52],"accomplished":[53],"through":[54],"robust":[56],"easy-to-extend":[58],"integration":[59],"arbitrary":[61,86],"python":[62],"processing":[64],"functions":[65],"into":[66],"labeling":[69],"process.":[70],"Importantly,":[71],"framework":[73],"devised":[74],"for":[75],"this":[76],"application":[77],"allows":[78],"easy":[79],"visualization":[80],"machine":[82],"learning":[83],"prediction":[84],"formats":[87],"amounts":[89],"per-component":[91],"metadata.":[92],"To":[93],"our":[94],"knowledge,":[95],"ease":[97],"flexibility":[99],"offered":[100],"are":[101],"unique":[102],"S3A":[104],"among":[105],"all":[106],"opensource":[107],"alternatives.":[108]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
