{"id":"https://openalex.org/W2807141747","doi":"https://doi.org/10.18653/v1/w18-2301","title":"Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility","display_name":"Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2807141747","doi":"https://doi.org/10.18653/v1/w18-2301","mag":"2807141747"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w18-2301","doi":"https://doi.org/10.18653/v1/w18-2301","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-2301","pdf_url":"https://www.aclweb.org/anthology/W18-2301.pdf","source":null,"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 BioNLP 2018 workshop","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W18-2301.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039754917","display_name":"Denis R. Newman-Griffis","orcid":"https://orcid.org/0000-0002-0473-4226","observed_orcids":["https://orcid.org/0000-0002-0473-4226"]},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]},{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]},{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Denis Newman-Griffis","raw_affiliation_strings":["Department of Computer Science and Engineering , The Ohio State University , Columbus , OH","Rehabilitation Medicine Department , Clinical Center , National Institutes of Health , Bethesda , MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering , The Ohio State University , Columbus , OH","institution_ids":["https://openalex.org/I52357470"]},{"raw_affiliation_string":"Rehabilitation Medicine Department , Clinical Center , National Institutes of Health , Bethesda , MD","institution_ids":["https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074138666","display_name":"Ayah Zirikly","orcid":"https://orcid.org/0000-0002-8441-1741","observed_orcids":["https://orcid.org/0000-0002-8441-1741"]},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]},{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ayah Zirikly","raw_affiliation_strings":["Rehabilitation Medicine Department , Clinical Center , National Institutes of Health , Bethesda , MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rehabilitation Medicine Department , Clinical Center , National Institutes of Health , Bethesda , MD","institution_ids":["https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1352,"has_fulltext":true,"cited_by_count":31,"citation_normalized_percentile":{"value":0.91438225,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5346999764442444,"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":0.5346999764442444,"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":0.37959998846054077,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0649000033736229,"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/patient-mobility","display_name":"patient mobility","score":0.800000011920929},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"named entity recognition","score":0.8349999785423279},{"id":"https://openalex.org/keywords/low-resource-ner","display_name":"low-resource NER","score":0.6190000176429749},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"domain adaptation","score":0.7770000100135803},{"id":"https://openalex.org/keywords/word-embeddings","display_name":"word embeddings","score":0.953000009059906},{"id":"https://openalex.org/keywords/electronic-health-records","display_name":"electronic health records","score":0.8360000252723694},{"id":"https://openalex.org/keywords/unstructured-text","display_name":"unstructured text","score":0.4309999942779541},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.8190000057220459},{"id":"https://openalex.org/keywords/functional-status","display_name":"functional status","score":0.4309999942779541},{"id":"https://openalex.org/keywords/clinical-text-extraction","display_name":"clinical text extraction","score":0.33000001311302185}],"concepts":[{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.8816318511962891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8015789985656738},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6713735461235046},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6230738759040833},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.579072892665863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5748852491378784},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5555574297904968},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5397295951843262},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.49205464124679565},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.48873424530029297},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4700058102607727},{"id":"https://openalex.org/C2777889803","wikidata":"https://www.wikidata.org/wiki/Q25047676","display_name":"Named entity","level":2,"score":0.46235784888267517},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.45694243907928467},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.4520284831523895},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4428779184818268},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.42815470695495605},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.42775407433509827},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.42367202043533325},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.11878341436386108},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.18653/v1/w18-2301","doi":"https://doi.org/10.18653/v1/w18-2301","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-2301","pdf_url":"https://www.aclweb.org/anthology/W18-2301.pdf","source":null,"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 BioNLP 2018 workshop","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:196493","doi":null,"is_oa":false,"landing_page_url":"https://eprints.whiterose.ac.uk/196493/1/2018_bionlp.pdf","pdf_url":"https://eprints.whiterose.ac.uk/196493/1/2018_bionlp.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Proceedings Paper"},{"id":"pmh:oai:arXiv.org:1806.02814","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.02814","pdf_url":"https://arxiv.org/pdf/1806.02814","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2807141747","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1806.02814.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1806.02814","doi":"https://doi.org/10.48550/arxiv.1806.02814","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1806.02814","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/w18-2301","doi":"https://doi.org/10.18653/v1/w18-2301","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-2301","pdf_url":"https://www.aclweb.org/anthology/W18-2301.pdf","source":null,"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 BioNLP 2018 workshop","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.6240000128746033,"id":"https://metadata.un.org/sdg/3"}],"sustainable_development_goals_aurora":[{"display_name":"Quality education","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/4"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320308309","display_name":"U.S. Social Security Administration","ror":"https://ror.org/04b7xxn32"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2807141747.pdf","grobid_xml":"https://content.openalex.org/works/W2807141747.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2963354094","https://openalex.org/W2995950854","https://openalex.org/W2980839612","https://openalex.org/W3023545062","https://openalex.org/W3201662413","https://openalex.org/W2558154128","https://openalex.org/W3015662889","https://openalex.org/W3023673030","https://openalex.org/W2128278816","https://openalex.org/W1561860492","https://openalex.org/W2779014967","https://openalex.org/W2940685834","https://openalex.org/W3088886289","https://openalex.org/W3003183647","https://openalex.org/W2978268358","https://openalex.org/W2974256357","https://openalex.org/W3149011051","https://openalex.org/W3128358461","https://openalex.org/W2982499086","https://openalex.org/W3087623576"],"abstract_inverted_index":{"Functioning":[0],"is":[1],"gaining":[2],"recognition":[3,48],"as":[4,44,96,98],"an":[5],"important":[6],"indicator":[7],"of":[8,24,28,35,55,73,126,134],"global":[9],"health,":[10],"but":[11],"remains":[12],"under-studied":[13],"in":[14,78,113,123,141],"medical":[15],"natural":[16],"language":[17],"processing":[18],"research.We":[19],"present":[20],"the":[21,42,53,130],"first":[22],"analysis":[23,118],"automatically":[25],"extracting":[26,124],"descriptions":[27,125],"patient":[29,65,127],"mobility,":[30,128],"using":[31],"a":[32,45,79,90],"recently-developed":[33],"dataset":[34],"free":[36],"text":[37,61],"electronic":[38],"health":[39],"records.We":[40],"frame":[41],"task":[43],"named":[46],"entity":[47],"(NER)":[49],"problem,":[50],"and":[51,105,116,132,137],"investigate":[52],"applicability":[54],"NER":[56,83],"techniques":[57,109],"to":[58],"mobility":[59,142],"extraction.As":[60],"corpora":[62],"focused":[63],"on":[64,89],"functioning":[66],"are":[67],"scarce,":[68],"we":[69],"explore":[70],"domain":[71,107],"adaptation":[72,108],"word":[74],"embeddings":[75,87],"for":[76],"use":[77],"recurrent":[80],"neural":[81],"network":[82],"system.We":[84],"find":[85],"that":[86,106],"trained":[88],"small":[91],"in-domain":[92],"corpus":[93],"perform":[94],"nearly":[95],"well":[97],"those":[99],"learned":[100],"from":[101],"large":[102],"out-ofdomain":[103],"corpora,":[104],"yield":[110],"additional":[111],"improvements":[112],"both":[114],"precision":[115],"recall.Our":[117],"identifies":[119],"several":[120],"significant":[121],"challenges":[122],"including":[129],"length":[131],"complexity":[133],"annotated":[135],"entities":[136],"high":[138],"linguistic":[139],"variability":[140],"descriptions.":[143]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":10}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-10T00:00:00"}