{"id":"https://openalex.org/W4379744446","doi":"https://doi.org/10.1145/3555776.3578592","title":"FREDA: Flexible Relation Extraction Data Annotation","display_name":"FREDA: Flexible Relation Extraction Data Annotation","publication_year":2023,"publication_date":"2023-03-27","ids":{"openalex":"https://openalex.org/W4379744446","doi":"https://doi.org/10.1145/3555776.3578592"},"language":"en","primary_location":{"id":"doi:10.1145/3555776.3578592","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3555776.3578592","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3555776.3578592","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3555776.3578592","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071126005","display_name":"Michael Strobl","orcid":"https://orcid.org/0000-0002-4062-1026"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Michael Strobl","raw_affiliation_strings":["University of Alberta, Edmonton, Canada"],"raw_orcid":"https://orcid.org/0000-0002-4062-1026","affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038712780","display_name":"Amine Trabelsi","orcid":"https://orcid.org/0000-0002-1852-5265"},"institutions":[{"id":"https://openalex.org/I135117807","display_name":"Universit\u00e9 de Sherbrooke","ror":"https://ror.org/00kybxq39","country_code":"CA","type":"education","lineage":["https://openalex.org/I135117807"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Amine Trabelsi","raw_affiliation_strings":["University de Sherbrooke, Sherbrooke, Canada"],"raw_orcid":"https://orcid.org/0000-0003-3322-4193","affiliations":[{"raw_affiliation_string":"University de Sherbrooke, Sherbrooke, Canada","institution_ids":["https://openalex.org/I135117807"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053745515","display_name":"Osmar R. Zai\u034f\u0308ane","orcid":"https://orcid.org/0000-0002-0060-5988"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Osmar Zaiane","raw_affiliation_strings":["University of Alberta, Edmonton, Canada"],"raw_orcid":"https://orcid.org/0000-0002-0060-5988","affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton, Canada","institution_ids":["https://openalex.org/I154425047"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05096706,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"902","last_page":"910"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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/T10181","display_name":"Natural Language Processing Techniques","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":0.9995999932289124,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.8540084362030029},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8427874445915222},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.8284589052200317},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.8140078783035278},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5750730633735657},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5369439125061035},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.500108003616333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47168901562690735},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.46678626537323},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.44790926575660706},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3539341688156128},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33617928624153137},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3355611264705658},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.04913914203643799}],"concepts":[{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.8540084362030029},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8427874445915222},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.8284589052200317},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.8140078783035278},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5750730633735657},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5369439125061035},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.500108003616333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47168901562690735},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.46678626537323},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.44790926575660706},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3539341688156128},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33617928624153137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3355611264705658},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.04913914203643799},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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":1,"locations":[{"id":"doi:10.1145/3555776.3578592","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3555776.3578592","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3555776.3578592","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3555776.3578592","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3555776.3578592","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3555776.3578592","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379744446.pdf","grobid_xml":"https://content.openalex.org/works/W4379744446.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2081580037","https://openalex.org/W2123442489","https://openalex.org/W2489487449","https://openalex.org/W2524643686","https://openalex.org/W2529518929","https://openalex.org/W2593831809","https://openalex.org/W2595551253","https://openalex.org/W2607303097","https://openalex.org/W2963777632","https://openalex.org/W2964022985","https://openalex.org/W2970986510","https://openalex.org/W3011411500","https://openalex.org/W3034891697","https://openalex.org/W3035566559","https://openalex.org/W4239019441","https://openalex.org/W4252110927","https://openalex.org/W4391156274"],"related_works":["https://openalex.org/W2976808399","https://openalex.org/W2609844752","https://openalex.org/W2981341912","https://openalex.org/W4285246823","https://openalex.org/W4226278302","https://openalex.org/W4385734297","https://openalex.org/W2547211086","https://openalex.org/W4221160509","https://openalex.org/W3114142812","https://openalex.org/W4380551175"],"abstract_inverted_index":{"To":[0],"effectively":[1],"train":[2],"accurate":[3],"Relation":[4,64,72],"Extraction":[5,65,73],"models,":[6,68],"sufficient":[7],"and":[8,21,82,107],"properly":[9],"labeled":[10,15],"data":[11,16,24],"is":[12,17,25,43],"required.":[13],"Adequately":[14],"difficult":[18],"to":[19,37,56,70,85,94],"obtain":[20],"annotating":[22],"such":[23],"a":[26,102,109],"tricky":[27],"undertaking.":[28],"Previous":[29],"works":[30],"have":[31],"shown":[32],"that":[33],"either":[34],"accuracy":[35],"has":[36],"be":[38],"sacrificed":[39],"or":[40],"the":[41,61,75],"task":[42,62],"extremely":[44],"time-consuming,":[45],"if":[46],"done":[47],"accurately.":[48],"We":[49],"are":[50],"proposing":[51],"an":[52],"approach":[53],"in":[54,101],"order":[55],"produce":[57],"high-quality":[58],"datasets":[59],"for":[60,98,114],"of":[63,105],"quickly.":[66],"Neural":[67],"trained":[69,108],"do":[71],"on":[74],"created":[76],"datasets,":[77],"achieve":[78],"very":[79],"good":[80],"results":[81],"generalize":[83],"well":[84],"other":[86],"datasets.":[87],"In":[88],"our":[89],"study,":[90],"we":[91],"were":[92],"able":[93],"annotate":[95],"10,022":[96],"sentences":[97],"19":[99],"relations":[100],"reasonable":[103],"amount":[104],"time,":[106],"commonly":[110],"used":[111],"baseline":[112],"model":[113],"each":[115],"relation.":[116]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}