Semantic Topic Modelling

Bibliographic Details
Main Author: Ferrugento, Adriana Figueiredo
Publication Date: 2015
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/10316/35724
Summary: Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra
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spelling Semantic Topic ModellingSemantic Topic ModellingDissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraTopic models came to improve the way search, browse and summarization of large sets of texts is performed. These models are used for uncovering the main theme of the documents in a corpus, where topics are probability distributions over a collection of words that is representative of a document. The most widely used topic model is called Latent Dirichlet Allocation (LDA) and it enables for documents to be characterized by more than one topic. This allows for a more accurate representation of what happens with real documents, where a text may have more than one underlying theme. However, this popular model is still far from producing excellent topics, given that it does not account for the semantic relations between words. It may thus result in redundant topics that contain di erent words, but with the same meaning. This thesis o ers a way to improve the LDA algorithm and, hence, solve the problem of not considering the semantics of words. The model proposed here uses the LDA algorithm as a starting point, however some changes are made, since it is our interest to introduce semantic relations in this model. A main component of the proposed model is the use of a lexical database for English, WordNet, which enables the integration of semantics by accessing its content.2015-07-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttps://hdl.handle.net/10316/35724https://hdl.handle.net/10316/35724TID:201537966engFerrugento, Adriana Figueiredoinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2022-05-25T04:35:56Zoai:estudogeral.uc.pt:10316/35724Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T05:12:56.889293Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Semantic Topic Modelling
title Semantic Topic Modelling
spellingShingle Semantic Topic Modelling
Ferrugento, Adriana Figueiredo
Semantic Topic Modelling
title_short Semantic Topic Modelling
title_full Semantic Topic Modelling
title_fullStr Semantic Topic Modelling
title_full_unstemmed Semantic Topic Modelling
title_sort Semantic Topic Modelling
author Ferrugento, Adriana Figueiredo
author_facet Ferrugento, Adriana Figueiredo
author_role author
dc.contributor.author.fl_str_mv Ferrugento, Adriana Figueiredo
dc.subject.por.fl_str_mv Semantic Topic Modelling
topic Semantic Topic Modelling
description Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra
publishDate 2015
dc.date.none.fl_str_mv 2015-07-15
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 https://hdl.handle.net/10316/35724
https://hdl.handle.net/10316/35724
TID:201537966
url https://hdl.handle.net/10316/35724
identifier_str_mv TID:201537966
dc.language.iso.fl_str_mv eng
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eu_rights_str_mv openAccess
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instacron_str RCAAP
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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