Resolu??o de correfer?ncia nominal usando sem?ntica em l?ngua portuguesa

• Ano de defesa: 2018
• Autor(a) principal: Fonseca, Evandro Brasil
• Orientador(a): Vieira, Renata
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Pontif?cia Universidade Cat?lica do Rio Grande do Sul
• Programa de Pós-Graduação: Programa de P?s-Gradua??o em Ci?ncia da Computa??o
• Departamento: Escola Polit?cnica
• País: Brasil
• Palavras-chave em Português: Resolu??o de Correfer?ncia; Extra??o de Informa??o
• Palavras-chave em Inglês: Coreference Resolution; Information Extraction
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/8169

Coreference Resolution task is challenging for Natural Language Processing, considering the required linguistic knowledge and the sophistication of language processing techniques involved. Even though it is a demanding task, a motivating factor in the study of this phenomenon is its usefulness. Basically, several Natural Language Processing tasks may benefit from their results, such as named entities recognition, relation extraction between named entities, summarization, sentiment analysis, among others. Coreference Resolution is a process that consists on identifying certain terms and expressions that refer to the same entity. For example, in the sentence ? France is refusing. The country is one of the first in the ranking... ? we can say that [the country] is a coreference of [France]. By grouping these referential terms, we form coreference groups, more commonly known as coreference chains. This thesis proposes a process for coreference resolution between noun phrases for Portuguese, focusing on the use of semantic knowledge. Our proposed approach is based on syntactic-semantic linguistic rules. That is, we combine different levels of linguistic processing, using semantic relations as support, in order to infer referential relations between mentions. Models based on linguistic rules have been efficiently applied in other languages, such as: English, Spanish and Galician. In few words, these models are more efficient than machine learning approaches when we deal with less resourceful languages, since the lack of sample-rich corpora may produce a poor training. The proposed approach is the first model for Portuguese coreference resolution which uses semantic knowledge. Thus, we consider it as the main contribution of this thesis.