Tradução automática estatística baseada em sintaxe e linguagens de árvores

• Ano de defesa: 2012
• Autor(a) principal: Beck, Daniel Emilio
• Orientador(a): Caseli, Helena de Medeiros
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• País: BR
• Palavras-chave em Português: Processamento da linguagem natural (Computação); Linguística - processamento de dados; Linguagem - tradução automática; Processamento da Língua Natural; Linguística Computacional; Tradução automática estatística; Gramáticas de substituição de árvores; Transdutores árvore-para-String
• Palavras-chave em Inglês: Natural language processing; Computational linguistics; Statistical machine translation; Tree substitution grammars; Tree-to-string transducers
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/504

Machine Translation (MT) is one of the classic Natural Language Processing (NLP) applications. The state-of-the-art in MT is represented by statistical methods that aim to learn all necessary linguistic knowledge automatically through large collections of texts (corpora). However, while the quality of statistical MT systems had improved, nowadays these advances are not significant. For this reason, research in the area have sought to involve more explicit linguistic knowledge in these systems. One issue that purely statistical MT systems have is the lack of correct treatment of syntactic phenomena. Thus, one of the research directions when trying to incorporate linguistic knowledge in those systems is through the addition of syntactic rules. To accomplish this, many methods and formalisms with this goal in mind are studied. This text presents the investigation of methods which aim to advance the state-of-the-art in statistical MT through models that consider syntactic information. The methods and formalisms studied are those used to deal with tree languages, mainly Tree Substitution Grammars (TSGs) and Tree-to-String (TTS) Transducers. From this work, a greater understanding was obtained about the studied formalisms and their behavior when used in NLP applications.