Sentiment classification using tree‐based gated recurrent units

Detalhes bibliográficos
Autor(a) principal: Tsakalos, Vasileios
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/33869
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Sentiment classification using tree‐based gated recurrent unitsDeep LearningNatural Language ProcessingRecursive Neural NetworksSentiment ClassificationDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNatural Language Processing is one of the most challenging fields of Artificial Intelligence. The past 10 years, this field has witnessed a fascinating progress due to Deep Learning. Despite that, we haven’t achieved to build an architecture of models that can understand natural language as humans do. Many architectures have been proposed, each of them having its own strengths and weaknesses. In this report, we will cover the tree based architectures and in particular we will propose a different tree based architecture that is very similar to the Tree-Based LSTM, proposed by Tai(2015). In this work, we aim to make a critical comparison between the proposed architecture -Tree-Based GRU- with Tree-based LSTM for sentiment classification tasks, both binary and fine-grained.Henriques, Roberto André PereiraRUNTsakalos, Vasileios2018-04-05T14:52:50Z2018-03-262018-03-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/33869TID:201894556enginfo: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:RCAAP2024-03-11T04:18:36Zoai:run.unl.pt:10362/33869Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:30:05.755444Repositório 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 Sentiment classification using tree‐based gated recurrent units
title Sentiment classification using tree‐based gated recurrent units
spellingShingle Sentiment classification using tree‐based gated recurrent units
Tsakalos, Vasileios
Deep Learning
Natural Language Processing
Recursive Neural Networks
Sentiment Classification
title_short Sentiment classification using tree‐based gated recurrent units
title_full Sentiment classification using tree‐based gated recurrent units
title_fullStr Sentiment classification using tree‐based gated recurrent units
title_full_unstemmed Sentiment classification using tree‐based gated recurrent units
title_sort Sentiment classification using tree‐based gated recurrent units
author Tsakalos, Vasileios
author_facet Tsakalos, Vasileios
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Tsakalos, Vasileios
dc.subject.por.fl_str_mv Deep Learning
Natural Language Processing
Recursive Neural Networks
Sentiment Classification
topic Deep Learning
Natural Language Processing
Recursive Neural Networks
Sentiment Classification
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2018
dc.date.none.fl_str_mv 2018-04-05T14:52:50Z
2018-03-26
2018-03-26T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/33869
TID:201894556
url http://hdl.handle.net/10362/33869
identifier_str_mv TID:201894556
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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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