Improving Personalized Consumer Health Search
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/24987 |
Resumo: | CLEF 2018 eHealth Consumer Health Search task aims to investigate the effectiveness of the information retrieval systems in providing health information to common health consumers. Compared to previous years, this year’s task includes five subtasks and adopts new data corpus and set of queries. This paper presents the work of University of Evora participating in two subtasks: IRtask-1 and IRtask-2. It explores the use of learning to rank techniques as well as query expan- sion approaches. A number of field based features are used for training a learning to rank model and a medical concept model proposed in previous work is re-employed for this year’s new task. Word vectors and UMLS are used as query expansion sources. Four runs were submitted to each task accordingly. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Improving Personalized Consumer Health Searchhealth information searchlearning to rankquery expansionUMLSword vectorsCLEF 2018 eHealth Consumer Health Search task aims to investigate the effectiveness of the information retrieval systems in providing health information to common health consumers. Compared to previous years, this year’s task includes five subtasks and adopts new data corpus and set of queries. This paper presents the work of University of Evora participating in two subtasks: IRtask-1 and IRtask-2. It explores the use of learning to rank techniques as well as query expan- sion approaches. A number of field based features are used for training a learning to rank model and a medical concept model proposed in previous work is re-employed for this year’s new task. Word vectors and UMLS are used as query expansion sources. Four runs were submitted to each task accordingly.CEUR2019-02-26T17:26:36Z2019-02-262018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/24987http://hdl.handle.net/10174/24987engHua Yang and Teresa Gonçalves. Improving personalized consumer health search: Note- book for ehealth at clef 2018. In Linda Cappellato, Nicola Ferro, Jian-Yun Nie, and Laure Soulier, editors, Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum, Avignon, France, September 10-14, 2018.ndnd498Yang, HuaGonçalves, Teresainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:18:24Zoai:dspace.uevora.pt:10174/24987Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:15:30.166138Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Improving Personalized Consumer Health Search |
title |
Improving Personalized Consumer Health Search |
spellingShingle |
Improving Personalized Consumer Health Search Yang, Hua health information search learning to rank query expansion UMLS word vectors |
title_short |
Improving Personalized Consumer Health Search |
title_full |
Improving Personalized Consumer Health Search |
title_fullStr |
Improving Personalized Consumer Health Search |
title_full_unstemmed |
Improving Personalized Consumer Health Search |
title_sort |
Improving Personalized Consumer Health Search |
author |
Yang, Hua |
author_facet |
Yang, Hua Gonçalves, Teresa |
author_role |
author |
author2 |
Gonçalves, Teresa |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Yang, Hua Gonçalves, Teresa |
dc.subject.por.fl_str_mv |
health information search learning to rank query expansion UMLS word vectors |
topic |
health information search learning to rank query expansion UMLS word vectors |
description |
CLEF 2018 eHealth Consumer Health Search task aims to investigate the effectiveness of the information retrieval systems in providing health information to common health consumers. Compared to previous years, this year’s task includes five subtasks and adopts new data corpus and set of queries. This paper presents the work of University of Evora participating in two subtasks: IRtask-1 and IRtask-2. It explores the use of learning to rank techniques as well as query expan- sion approaches. A number of field based features are used for training a learning to rank model and a medical concept model proposed in previous work is re-employed for this year’s new task. Word vectors and UMLS are used as query expansion sources. Four runs were submitted to each task accordingly. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01T00:00:00Z 2019-02-26T17:26:36Z 2019-02-26 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/24987 http://hdl.handle.net/10174/24987 |
url |
http://hdl.handle.net/10174/24987 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Hua Yang and Teresa Gonçalves. Improving personalized consumer health search: Note- book for ehealth at clef 2018. In Linda Cappellato, Nicola Ferro, Jian-Yun Nie, and Laure Soulier, editors, Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum, Avignon, France, September 10-14, 2018. nd nd 498 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
CEUR |
publisher.none.fl_str_mv |
CEUR |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136636599009280 |