{"id":"https://openalex.org/W4285138249","doi":"https://doi.org/10.32604/iasc.2022.030794","title":"Employing Lexicalized Dependency Paths for Active Learning of Relation Extraction","display_name":"Employing Lexicalized Dependency Paths for Active Learning of Relation Extraction","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285138249","doi":"https://doi.org/10.32604/iasc.2022.030794"},"language":"en","primary_location":{"id":"doi:10.32604/iasc.2022.030794","is_oa":true,"landing_page_url":"https://doi.org/10.32604/iasc.2022.030794","pdf_url":"https://www.techscience.com/iasc/v34n3/47953/pdf","source":{"id":"https://openalex.org/S40639465","display_name":"Intelligent Automation & Soft Computing","issn_l":"1079-8587","issn":["1079-8587","2326-005X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Automation &amp; Soft Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.techscience.com/iasc/v34n3/47953/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088362800","display_name":"Huiyu Sun","orcid":"https://orcid.org/0000-0002-0631-3468"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Huiyu Sun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5004580149","display_name":"Ralph Grishman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ralph Grishman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088362800"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.355,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.97561348,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":"34","issue":"3","first_page":"1415","last_page":"1423"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9919000267982483,"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":0.9919000267982483,"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":0.9794999957084656,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9779999852180481,"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":0.8758091926574707},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.7828473448753357},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.7317564487457275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6369167566299438},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6339012980461121},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.6298503875732422},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5694316029548645},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5404762625694275},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5400882363319397},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4768292009830475},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4169832766056061},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.2880534529685974},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.20945262908935547}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8758091926574707},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.7828473448753357},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.7317564487457275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6369167566299438},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6339012980461121},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.6298503875732422},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5694316029548645},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5404762625694275},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5400882363319397},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4768292009830475},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4169832766056061},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2880534529685974},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.20945262908935547},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/iasc.2022.030794","is_oa":true,"landing_page_url":"https://doi.org/10.32604/iasc.2022.030794","pdf_url":"https://www.techscience.com/iasc/v34n3/47953/pdf","source":{"id":"https://openalex.org/S40639465","display_name":"Intelligent Automation & Soft Computing","issn_l":"1079-8587","issn":["1079-8587","2326-005X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Automation &amp; Soft Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/iasc.2022.030794","is_oa":true,"landing_page_url":"https://doi.org/10.32604/iasc.2022.030794","pdf_url":"https://www.techscience.com/iasc/v34n3/47953/pdf","source":{"id":"https://openalex.org/S40639465","display_name":"Intelligent Automation & Soft Computing","issn_l":"1079-8587","issn":["1079-8587","2326-005X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Automation &amp; Soft Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2083210304","https://openalex.org/W2135006190","https://openalex.org/W2155454737","https://openalex.org/W2539896663","https://openalex.org/W2587809655","https://openalex.org/W2611268543","https://openalex.org/W4285147355","https://openalex.org/W4320013820","https://openalex.org/W6623085689","https://openalex.org/W6631255695","https://openalex.org/W6640685283","https://openalex.org/W6665633857","https://openalex.org/W6676040603","https://openalex.org/W6678358565","https://openalex.org/W6678778854","https://openalex.org/W6680353034","https://openalex.org/W6691304660","https://openalex.org/W6691308127","https://openalex.org/W6691368528","https://openalex.org/W6691478959","https://openalex.org/W6691546939","https://openalex.org/W6691723933","https://openalex.org/W6691864264","https://openalex.org/W6737846962","https://openalex.org/W6754733053","https://openalex.org/W6755072339","https://openalex.org/W6760892103","https://openalex.org/W6766111062","https://openalex.org/W6779757780","https://openalex.org/W6780251962","https://openalex.org/W6797137753","https://openalex.org/W6797791348","https://openalex.org/W6798317632","https://openalex.org/W6802085312","https://openalex.org/W6803758503","https://openalex.org/W6803907844","https://openalex.org/W6940380394"],"related_works":["https://openalex.org/W2250721602","https://openalex.org/W2056314584","https://openalex.org/W1492505081","https://openalex.org/W1986633584","https://openalex.org/W2976807053","https://openalex.org/W184546935","https://openalex.org/W2909115059","https://openalex.org/W2943886396","https://openalex.org/W4378501087","https://openalex.org/W3203050224"],"abstract_inverted_index":{"Active":[0],"learning":[1,15,91,107,123],"methods":[2],"which":[3],"present":[4],"selected":[5],"examples":[6],"from":[7],"the":[8,24,27,36,44,49,55,128],"<i>corpus</i>":[9],"for":[10,80,100],"annotation":[11,110],"provide":[12],"more":[13],"efficient":[14],"of":[16,30,117,134],"supervised":[17,118],"relation":[18],"extraction":[19,56],"models,":[20],"but":[21],"they":[22],"leave":[23],"developer":[25,50],"in":[26],"unenviable":[28],"role":[29,39],"a":[31,41,67,97,131],"passive":[32],"informant.":[33],"To":[34],"restore":[35],"developer\u2019s":[37],"proper":[38],"as":[40,94],"partner":[42],"with":[43,76,92],"system,":[45],"we":[46],"must":[47],"give":[48],"an":[51,77],"ability":[52],"to":[53,62,85,127],"inspect":[54],"model":[57],"during":[58],"development.":[59],"We":[60,82],"propose":[61],"make":[63],"this":[64],"possible":[65],"through":[66],"representation":[68],"based":[69],"on":[70],"lexicalized":[71],"dependency":[72],"paths":[73],"(LDPs)":[74],"coupled":[75],"active":[78,90,106,122],"learner":[79],"LDPs.":[81],"apply":[83],"LDPs":[84],"both":[86],"simulated":[87,105],"and":[88,96,102],"real":[89,121],"ACE":[93],"evaluation":[95],"year\u2019s":[98],"newswire":[99],"training":[101],"show":[103],"that":[104],"greatly":[108],"reduces":[109],"cost":[111],"while":[112,120],"maintaining":[113],"similar":[114],"performance":[115,126],"level":[116],"learning,":[119],"yields":[124],"comparable":[125],"state-of-the-art":[129],"using":[130],"small":[132],"number":[133],"annotations.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":32}],"updated_date":"2026-05-20T08:49:12.498775","created_date":"2025-10-10T00:00:00"}