2020-12-302020-12-302020-11-17BARROS, C. C. Acelerando a construção de tabelas hash para dados textuais com aplicações. 2020. 99 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2020.http://repositorio.bc.ufg.br/tede/handle/tede/11006Text mining is characterized by the extraction of information from textual data, in the most diverse formats, aiming at the knowledge production, classification, clusterization, translation of this information among other things. In order for text mining to be efficient, some procedures are performed on the data to ensure that it contains only content relevant to the analysis to be performed, and that it is structured in a format that is easier to manipulate computationally. Several pre-processing tasks must be performed on this data, in order to achieve the desired quality and representation. In this sense, the present work proposes an implementation of a hash table capable of efficiently exploring the high parallelism available in GPUs, as a way to increase the performance of pre- processing tasks. However, this work not only presents more efficient algorithms, but also demonstrates the feasibility of its use in applications such as the generation of the co- occurrence matrix and the representation of the text using embeddings.Attribution-NonCommercial-NoDerivatives 4.0 InternationalComputação de alto desempenhoAprendizado de máquinaMineração de textoMatriz de coocorrênciaTabelas hashEmbeddingsCUDAHpcCUDAMachine learningHigh performance computingText miningCo-occurrence matrixHash tablesEmbeddingsCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAODissertação