Improving understandability in consumer health information search: Uevora @ 2016 fire chis
Autor(a) principal: | |
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Data de Publicação: | 2016 |
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/10174/20671 |
Resumo: | This paper presents our work at 2016 FIRE CHIS. Given a CHIS query and a document associated with that query, the task is to classify the sentences in the document as relevant to the query or not; and further classify the relevant sentences to be supporting, neutral or opposing to the claim made in the query. In this paper, we present two different approaches to do the classification. With the first approach, we implement two models to satisfy the task. We first implement an information retrieval model to retrieve the sentences that are relevant to the query; and then we use supervised learning method to train a classification model to classify the relevant sentences into support, oppose or neutral. With the second approach, we only use machine learning techniques to learn a model and classify the sentences into four classes (relevant & support, relevant & neutral, relevant & oppose, irrelevant & neutral). Our submission for CHIS uses the first approach. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Improving understandability in consumer health information search: Uevora @ 2016 fire chisThis paper presents our work at 2016 FIRE CHIS. Given a CHIS query and a document associated with that query, the task is to classify the sentences in the document as relevant to the query or not; and further classify the relevant sentences to be supporting, neutral or opposing to the claim made in the query. In this paper, we present two different approaches to do the classification. With the first approach, we implement two models to satisfy the task. We first implement an information retrieval model to retrieve the sentences that are relevant to the query; and then we use supervised learning method to train a classification model to classify the relevant sentences into support, oppose or neutral. With the second approach, we only use machine learning techniques to learn a model and classify the sentences into four classes (relevant & support, relevant & neutral, relevant & oppose, irrelevant & neutral). Our submission for CHIS uses the first approach.Erasmus Mundus LEADER projectCEUR2017-02-06T12:08:01Z2017-02-062016-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/20671http://hdl.handle.net/10174/20671engHua Yang and Teresa Gonc ̧alves. Improving understandability in consumer health information search: Uevora @ 2016 fire chis. In Prasenjit Majum- der, Mandar Mitra, Parth Mehta, Jainisha Sankhavara, and Kripabandhu Ghosh, editors, Working notes of FIRE 2016 – Forum for Information Retrieval Evaluation, volume 1737, pages 228–232, Kolkata, IN, December 2016. CEUR.ndtcg@uevora.pt498Yang, 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:RCAAP2024-01-03T19:10:37Zoai:dspace.uevora.pt:10174/20671Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:12:02.638853Repositório 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 understandability in consumer health information search: Uevora @ 2016 fire chis |
title |
Improving understandability in consumer health information search: Uevora @ 2016 fire chis |
spellingShingle |
Improving understandability in consumer health information search: Uevora @ 2016 fire chis Yang, Hua |
title_short |
Improving understandability in consumer health information search: Uevora @ 2016 fire chis |
title_full |
Improving understandability in consumer health information search: Uevora @ 2016 fire chis |
title_fullStr |
Improving understandability in consumer health information search: Uevora @ 2016 fire chis |
title_full_unstemmed |
Improving understandability in consumer health information search: Uevora @ 2016 fire chis |
title_sort |
Improving understandability in consumer health information search: Uevora @ 2016 fire chis |
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 |
description |
This paper presents our work at 2016 FIRE CHIS. Given a CHIS query and a document associated with that query, the task is to classify the sentences in the document as relevant to the query or not; and further classify the relevant sentences to be supporting, neutral or opposing to the claim made in the query. In this paper, we present two different approaches to do the classification. With the first approach, we implement two models to satisfy the task. We first implement an information retrieval model to retrieve the sentences that are relevant to the query; and then we use supervised learning method to train a classification model to classify the relevant sentences into support, oppose or neutral. With the second approach, we only use machine learning techniques to learn a model and classify the sentences into four classes (relevant & support, relevant & neutral, relevant & oppose, irrelevant & neutral). Our submission for CHIS uses the first approach. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-09-01T00:00:00Z 2017-02-06T12:08:01Z 2017-02-06 |
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/10174/20671 http://hdl.handle.net/10174/20671 |
url |
http://hdl.handle.net/10174/20671 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Hua Yang and Teresa Gonc ̧alves. Improving understandability in consumer health information search: Uevora @ 2016 fire chis. In Prasenjit Majum- der, Mandar Mitra, Parth Mehta, Jainisha Sankhavara, and Kripabandhu Ghosh, editors, Working notes of FIRE 2016 – Forum for Information Retrieval Evaluation, volume 1737, pages 228–232, Kolkata, IN, December 2016. CEUR. nd tcg@uevora.pt 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) |
collection |
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 |
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1799136602465763328 |