{"id":"https://openalex.org/W2914316363","doi":"https://doi.org/10.1109/bibm.2018.8621579","title":"GrantExtractor: A Winning System for Extracting Grant Support Information from Biomedical Literature","display_name":"GrantExtractor: A Winning System for Extracting Grant Support Information from Biomedical Literature","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2914316363","doi":"https://doi.org/10.1109/bibm.2018.8621579","mag":"2914316363"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2018.8621579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2018.8621579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032461169","display_name":"Suyang Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Suyang Dai","raw_affiliation_strings":["School of Computer Scince, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Scince, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100410554","display_name":"Zihan Zhang","orcid":"https://orcid.org/0000-0002-8099-5081"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihan Zhang","raw_affiliation_strings":["School of Computer Scince, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Scince, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061414495","display_name":"Wenxuan Zuo","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxuan Zuo","raw_affiliation_strings":["School of Computer Scince, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Scince, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072380374","display_name":"Xiaodi Huang","orcid":"https://orcid.org/0000-0002-6084-1851"},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiaodi Huang","raw_affiliation_strings":["School of Computing and Mathematics, Charles Sturt University, Albury, NSW, China"],"affiliations":[{"raw_affiliation_string":"School of Computing and Mathematics, Charles Sturt University, Albury, NSW, China","institution_ids":["https://openalex.org/I153230381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045866167","display_name":"Shanfeng Zhu","orcid":"https://orcid.org/0000-0002-6067-5312"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanfeng Zhu","raw_affiliation_strings":["School of Computer Scince, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Scince, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5032461169"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.0919,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4930768,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"3","issue":null,"first_page":"333","last_page":"340"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9976999759674072,"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":0.9703999757766724,"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":0.7008473873138428},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5990374088287354},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.536300778388977},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5316909551620483},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.526984453201294},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.516897439956665},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5043383836746216},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.49061980843544006},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.48564302921295166},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.46245265007019043},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4548017978668213},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.4496753513813019},{"id":"https://openalex.org/keywords/biomedical-text-mining","display_name":"Biomedical text mining","score":0.4493187665939331},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.42593914270401},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4091285765171051},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3942282795906067},{"id":"https://openalex.org/keywords/text-mining","display_name":"Text mining","score":0.17786303162574768},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.11412510275840759},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.11368590593338013}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7008473873138428},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5990374088287354},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.536300778388977},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5316909551620483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.526984453201294},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.516897439956665},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5043383836746216},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.49061980843544006},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.48564302921295166},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.46245265007019043},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4548017978668213},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.4496753513813019},{"id":"https://openalex.org/C165141518","wikidata":"https://www.wikidata.org/wiki/Q4915126","display_name":"Biomedical text mining","level":3,"score":0.4493187665939331},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.42593914270401},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4091285765171051},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3942282795906067},{"id":"https://openalex.org/C71472368","wikidata":"https://www.wikidata.org/wiki/Q676880","display_name":"Text mining","level":2,"score":0.17786303162574768},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.11412510275840759},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.11368590593338013},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2018.8621579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2018.8621579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320332505","display_name":"U.S. Public Health Service","ror":"https://ror.org/05xf94514"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W90362830","https://openalex.org/W218702423","https://openalex.org/W1522301498","https://openalex.org/W1940872118","https://openalex.org/W1981208470","https://openalex.org/W1997995752","https://openalex.org/W2053238041","https://openalex.org/W2064675550","https://openalex.org/W2079735306","https://openalex.org/W2096525273","https://openalex.org/W2097052916","https://openalex.org/W2114361266","https://openalex.org/W2118585731","https://openalex.org/W2124208536","https://openalex.org/W2143017621","https://openalex.org/W2147880316","https://openalex.org/W2296283641","https://openalex.org/W2574454672","https://openalex.org/W2734608416","https://openalex.org/W2769387903","https://openalex.org/W2953320089","https://openalex.org/W2963571188","https://openalex.org/W2964121744","https://openalex.org/W2977393556","https://openalex.org/W4243601081","https://openalex.org/W6603694148","https://openalex.org/W6631190155","https://openalex.org/W6640362995","https://openalex.org/W6677656871","https://openalex.org/W6682082992","https://openalex.org/W6732104566","https://openalex.org/W6750644136","https://openalex.org/W6764970959"],"related_works":["https://openalex.org/W842810586","https://openalex.org/W4319940250","https://openalex.org/W2352298027","https://openalex.org/W2092919065","https://openalex.org/W3138801416","https://openalex.org/W4236762297","https://openalex.org/W2369351710","https://openalex.org/W2489933339","https://openalex.org/W2572241437","https://openalex.org/W4379379356"],"abstract_inverted_index":{"As":[0],"the":[1,57,118,162,173,189,193,208,217],"important":[2],"information":[3,47,62],"in":[4,25,91,165,216],"MEDLINE":[5],"database,":[6],"grant":[7,102,139,221],"support":[8],"(GS)":[9],"refers":[10],"to":[11,43,56,67],"funding":[12,18,28,46,89,111],"agencies":[13,105],"and":[14,72,104,117],"contract":[15],"numbers.":[16],"For":[17],"organizations,":[19],"GS":[20,61],"plays":[21],"a":[22,35,53,97,114,132],"crucial":[23],"role":[24],"tracking":[26],"their":[27],"outcomes.":[29],"In":[30,87,158],"this":[31],"paper,":[32],"we":[33,135],"present":[34],"pipeline":[36],"system":[37],"called":[38],"GrantExtractor":[39,51,152,160,205],"that":[40,151],"is":[41,52,65,182,188],"able":[42],"automatically":[44],"extract":[45],"from":[48,109],"biomedical":[49],"literature.":[50],"novel":[54],"solution":[55],"practical":[58],"problem":[59],"of":[60,81,101,168,175,201,219],"extraction,":[63],"which":[64,187],"related":[66],"both":[68],"name":[69],"entity":[70],"recognition":[71],"relation":[73],"extraction.":[74],"Our":[75],"approaches":[76],"rely":[77],"on":[78,147],"an":[79],"integration":[80],"several":[82],"modern":[83],"machine":[84],"learning":[85],"techniques.":[86],"particular,":[88],"sentences":[90,112],"articles":[92],"are":[93,106],"first":[94,163],"identified":[95],"by":[96,113,131,198],"sentence":[98],"classifier.":[99],"Entities":[100],"numbers":[103,130],"then":[107],"extracted":[108],"these":[110],"bi-directional":[115],"LSTM":[116],"CRF":[119],"layer":[120],"(BiLSTM-CRF),":[121],"as":[122,124,192,212,214],"well":[123],"pattern":[125],"matching.":[126],"After":[127],"removing":[128],"noisy":[129],"multi-class":[133],"model,":[134],"finally":[136],"match":[137],"each":[138],"number":[140,181],"with":[141],"its":[142],"corresponding":[143],"agency.":[144],"Experimental":[145],"results":[146],"benchmark":[148],"datasets":[149],"show":[150],"clearly":[153],"outperformed":[154],"all":[155],"baseline":[156,194],"methods.":[157],"addition,":[159],"won":[161],"place":[164],"Task":[166],"5C":[167],"2017":[169],"BioASQ":[170],"challenge,":[171],"achieving":[172],"Micro-recall":[174],"0.9526":[176],"for":[177],"22,610":[178],"articles.":[179],"This":[180],"33%":[183],"higher":[184],"than":[185],"0.7174,":[186],"highest":[190],"score":[191,211],"of\u201cBioASQ":[195],"Filtering\u201d":[196],"provided":[197],"National":[199],"Library":[200],"Medicine":[202],"(NLM).":[203],"Moreover,":[204],"has":[206],"achieved":[207],"Micro":[209],"F-measure":[210],"high":[213],"0.90":[215],"task":[218],"extracting":[220],"pairs.":[222]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}