{"id":"https://openalex.org/W2027233318","doi":"https://doi.org/10.1145/2390068.2390073","title":"Clinical entity recognition using structural support vector machines with rich features","display_name":"Clinical entity recognition using structural support vector machines with rich features","publication_year":2012,"publication_date":"2012-10-29","ids":{"openalex":"https://openalex.org/W2027233318","doi":"https://doi.org/10.1145/2390068.2390073","mag":"2027233318"},"language":"en","primary_location":{"id":"doi:10.1145/2390068.2390073","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2390068.2390073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics","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/A5014238603","display_name":"Buzhou Tang","orcid":"https://orcid.org/0000-0003-0271-8246"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Buzhou Tang","raw_affiliation_strings":["Vanderbilt University, Nashville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, TN, USA","institution_ids":["https://openalex.org/I200719446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100526958","display_name":"Hongxin Cao","orcid":"https://orcid.org/0000-0001-6277-0762"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]},{"id":"https://openalex.org/I177933477","display_name":"Second Military Medical University","ror":"https://ror.org/04tavpn47","country_code":"CN","type":"education","lineage":["https://openalex.org/I177933477"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Hongxin Cao","raw_affiliation_strings":["Vanderbilt University, Nashville, TN, USA & Second Military Medical University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, TN, USA & Second Military Medical University, Shanghai, China","institution_ids":["https://openalex.org/I177933477","https://openalex.org/I200719446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010253402","display_name":"Yonghui Wu","orcid":"https://orcid.org/0000-0002-6780-6135"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yonghui Wu","raw_affiliation_strings":["Vanderbilt University, Nashville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, TN, USA","institution_ids":["https://openalex.org/I200719446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055848133","display_name":"Min Jiang","orcid":"https://orcid.org/0000-0002-1407-0961"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Min Jiang","raw_affiliation_strings":["Vanderbilt University, Nashville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, TN, USA","institution_ids":["https://openalex.org/I200719446"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084072550","display_name":"Hua Xu","orcid":"https://orcid.org/0000-0002-5274-4672"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hua Xu","raw_affiliation_strings":["Vanderbilt University, Nashville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, TN, USA","institution_ids":["https://openalex.org/I200719446"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5014238603"],"corresponding_institution_ids":["https://openalex.org/I200719446"],"apc_list":null,"apc_paid":null,"fwci":9.3642,"has_fulltext":false,"cited_by_count":57,"citation_normalized_percentile":{"value":0.97875681,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"13","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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":0.9986000061035156,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9958000183105469,"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/named-entity-recognition","display_name":"Named-entity recognition","score":0.9281336069107056},{"id":"https://openalex.org/keywords/crfs","display_name":"CRFS","score":0.9264792203903198},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.8906636238098145},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8060437440872192},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.76838219165802},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7624736428260803},{"id":"https://openalex.org/keywords/sequence-labeling","display_name":"Sequence labeling","score":0.6354955434799194},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5946498513221741},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5876902937889099},{"id":"https://openalex.org/keywords/biomedical-text-mining","display_name":"Biomedical text mining","score":0.5662977695465088},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5133244395256042},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.5125955939292908},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4999833106994629},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41232725977897644},{"id":"https://openalex.org/keywords/text-mining","display_name":"Text mining","score":0.29718902707099915}],"concepts":[{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.9281336069107056},{"id":"https://openalex.org/C2775953691","wikidata":"https://www.wikidata.org/wiki/Q5013874","display_name":"CRFS","level":3,"score":0.9264792203903198},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.8906636238098145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8060437440872192},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.76838219165802},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7624736428260803},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.6354955434799194},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5946498513221741},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5876902937889099},{"id":"https://openalex.org/C165141518","wikidata":"https://www.wikidata.org/wiki/Q4915126","display_name":"Biomedical text mining","level":3,"score":0.5662977695465088},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5133244395256042},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5125955939292908},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4999833106994629},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41232725977897644},{"id":"https://openalex.org/C71472368","wikidata":"https://www.wikidata.org/wiki/Q676880","display_name":"Text mining","level":2,"score":0.29718902707099915},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2390068.2390073","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2390068.2390073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8600000143051147,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332530","display_name":"Medical Center, University of Pittsburgh","ror":"https://ror.org/04ehecz88"},{"id":"https://openalex.org/F4320337351","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W38597370","https://openalex.org/W130850236","https://openalex.org/W1425787433","https://openalex.org/W1504212872","https://openalex.org/W1602694398","https://openalex.org/W1834104634","https://openalex.org/W1973031260","https://openalex.org/W1988995507","https://openalex.org/W2008830554","https://openalex.org/W2031248101","https://openalex.org/W2040909025","https://openalex.org/W2061848042","https://openalex.org/W2104091251","https://openalex.org/W2104381725","https://openalex.org/W2105842272","https://openalex.org/W2107435951","https://openalex.org/W2107934484","https://openalex.org/W2111437636","https://openalex.org/W2112670696","https://openalex.org/W2114039834","https://openalex.org/W2114388055","https://openalex.org/W2114668172","https://openalex.org/W2121227244","https://openalex.org/W2121740558","https://openalex.org/W2122402213","https://openalex.org/W2125260686","https://openalex.org/W2128279348","https://openalex.org/W2146089916","https://openalex.org/W2152540201","https://openalex.org/W2158139315","https://openalex.org/W2166861608","https://openalex.org/W2168041406","https://openalex.org/W2288093748","https://openalex.org/W4252684946","https://openalex.org/W6678277124"],"related_works":["https://openalex.org/W1964783010","https://openalex.org/W2962906565","https://openalex.org/W2399696375","https://openalex.org/W2798423868","https://openalex.org/W2061834489","https://openalex.org/W2062502130","https://openalex.org/W4250494529","https://openalex.org/W2132038179","https://openalex.org/W189110383","https://openalex.org/W2027233318"],"abstract_inverted_index":{"Named":[0],"entity":[1,52,152,176],"recognition":[2],"(NER)":[3],"is":[4,30],"an":[5,60],"important":[6],"task":[7,91],"for":[8,75,150],"natural":[9],"language":[10],"processing":[11],"(NLP)":[12],"of":[13,66,92,104,174,191,197],"clinical":[14,51,76,96,151,175],"text.":[15],"Conditional":[16],"Random":[17],"Fields":[18],"(CRFs),":[19],"a":[20,188],"sequential":[21],"labeling":[22],"algorithm,":[23],"and":[24,69,108,121],"Support":[25,56],"Vector":[26,57],"Machines":[27,58],"(SVMs),":[28],"which":[29,99,202],"based":[31],"on":[32,193],"large":[33],"margin":[34],"theory,":[35],"are":[36],"two":[37],"typical":[38],"machine":[39],"learning":[40],"algorithms":[41],"that":[42,62,133,163],"have":[43],"been":[44,73],"widely":[45],"applied":[46,83],"to":[47,87,101],"NER":[48,136],"tasks,":[49],"including":[50],"recognition.":[53,177],"However,":[54],"Structural":[55],"(SSVMs),":[59],"algorithm":[61,86],"combines":[63],"the":[64,84,88,93,115,128,134,147,172,194,204,209],"advantages":[65],"both":[67],"CRFs":[68],"SVMs,":[70],"has":[71],"not":[72],"investigated":[74],"text":[77],"processing.":[78],"In":[79],"this":[80],"study,":[81],"we":[82],"SSVMs":[85],"Concept":[89],"Extraction":[90],"2010":[94,198],"i2b2":[95,199],"NLP":[97,200],"challenge,":[98,129,201],"was":[100],"recognize":[102],"entities":[103],"medical":[105],"problems,":[106],"treatments,":[107],"tests":[109],"from":[110],"hospital":[111],"discharge":[112],"summaries.":[113],"Using":[114],"same":[116,155],"training":[117,140],"(N":[118,123],"=":[119,124],"27,837)":[120],"test":[122,195],"45,009)":[125],"sets":[126],"in":[127,208],"our":[130,185],"evaluation":[131],"showed":[132],"SSVMs-based":[135],"system":[137,149,186,206],"required":[138],"less":[139],"time,":[141],"while":[142],"achieved":[143,187],"better":[144],"performance":[145,173],"than":[146],"CRFs-based":[148],"recognition,":[153],"when":[154],"features":[156,165,180],"were":[157,181],"used.":[158],"Our":[159],"study":[160],"also":[161],"demonstrated":[162],"rich":[164,179],"such":[166],"as":[167],"unsupervised":[168],"word":[169],"representations":[170],"improved":[171],"When":[178],"integrated":[182],"with":[183],"SSVMs,":[184],"highest":[189],"F-measure":[190],"85.74%":[192],"set":[196],"outperformed":[203],"best":[205],"reported":[207],"challenge":[210],"by":[211],"0.5%.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}