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Title: | Uma abordagem em cascata para predição de gênero a partir de textos em Português |
Other Titles: | A cascading approach to gender prediction from portuguese texts |
Authors: | Merschmann, Luiz Henrique de Campos Pereira, Denilson Alves Paiva, Elaine Ribeiro de Faria |
Keywords: | Caracterização autoral Mineração de texto Predição de gênero Língua portuguesa Author profiling Portuguese language Text mining Gender prediction |
Issue Date: | 7-Feb-2022 |
Publisher: | Universidade Federal de Lavras |
Citation: | MORAIS, J. P. M. de. Uma abordagem em cascata para predição de gênero a partir de textos em Português. 2021. 48 p. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de Lavras, Lavras, 2022. |
Abstract: | Author Profiling, whose objective is the analysis of a text to uncover characteristics (e.g., gen- der and age) of its author, has become an important task in different areas such as forensics, marketing, and e-commerce. Although a lot of research has been conducted on this task for some widely used lan- guages (e.g., English), there is still a lot of room for improvement in studies involving the Portuguese language. Thus, this work contributes by proposing and evaluating a cascading approach, which combi- nes a weighted lexical approach, a heuristic and a classifier, for the gender prediction problem using only textual content written in the Portuguese language. The proposed approach takes into account both spe- cificities of the Portuguese language and domain characteristics of the texts. The results obtained from the proposed approach showed that exploring the specificities of the Portuguese language and domain characteristics of the texts can positively contribute to the performance of the gender prediction task. |
URI: | http://repositorio.ufla.br/jspui/handle/1/49191 |
Appears in Collections: | Ciência da Computação - Mestrado (Dissertações) |
Files in This Item:
File | Description | Size | Format | |
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DISSERTAÇÃO_Uma abordagem em cascata para predição de gênero a partir de textos em Português.pdf | 1,95 MB | Adobe PDF | View/Open |
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