Identificando plágio externo com Locality-sensitive hashing

• Ano de defesa: 2017
• Autor(a) principal: Duarte, Fellipe Ribeiro
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia de Sistemas e Computação; UFRJ
• Palavras-chave em Português: Engenharia de Sistemas e Computação; Recuperação da informação; Plágio; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://hdl.handle.net/11422/10158

Heuristic Retrieval task aims to retrieve a set of documents from which the external plagiarism detection identifies plagiarized pieces of text. In this context, we present Minmax Circular Sector Arcs algorithms that treats HR task as an approximate k-nearest neighbor search problem. Moreover, Minmax Circular Sector Arcs algorithms aim to retrieve the set of documents with greater amounts of plagiarized fragments, while reducing the amount of time to accomplish the HR task. Our theoretical framework is based on two aspects: (i) a triangular property to encode a range of sketches on a unique value; and (ii) a Circular Sector Arc property which enables (i) to be more accurate. Both properties were proposed for handling high-dimensional spaces, hashing them to a lower number of hash values. Our two Minmax Circular Sector Arcs methods, Minmax Circular Sector Arcs Lower Bound and Minmax Circular Sector Arcs Full Bound, achieved Recall levels slightly more imprecise than Minmaxwise hashing in exchange for a better Speedup in document indexing and query extraction and retrieval time in high-dimensional plagiarism related datasets.