Integration of non-geometric data in model-based information systems
Image classification combining visual features and text data: neural approach and ...
Distributed vector representation of documents applied to categorize short and noi...
Grant number: | 19/25010-5 |
Support Opportunities: | Regular Research Grants |
Duration: | November 01, 2020 - April 30, 2023 |
Field of knowledge: | Physical Sciences and Mathematics - Computer Science |
Principal Investigator: | Solange Oliveira Rezende |
Grantee: | Solange Oliveira Rezende |
Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
Associated researchers: | Alípio Mário Guedes Jorge ; Bruno Magalhães Nogueira ; Camila Vaccari Sundermann ; Marcos Aurelio Domingues ; Rafael Geraldeli Rossi ; Ricardo Marcondes Marcacini ; Roberta Akemi Sinoara ; Veronica Oliveira de Carvalho |
Abstract
Text Mining techniques have become essential for supporting text analysis and knowledge discovery as the volume and variety of digital text documents have increased, either in social networks and the Web or inside organizations. Despite the application task or applied technique, the treatment of text semantics is an important challenge of the Text Mining process. The challenge is even bigger when we analyze Portuguese texts due to language particularities and the low number of Portuguese resources and researches. In this context, this project aims to advance Text Mining research, focusing on the Portuguese language, and disseminate the knowledge of the field by applying Text Mining techniques in different real-world problems. We will investigate and propose semantically enriched text representation models, considering both the vector-space model and network-based representations, as well as their application in one-class learning. As a first step to support this research, we will collect, prepare and characterize collections of texts written in Portuguese, and make consolidated information about labeled collections available to the research community. Lastly, we will evaluate and apply semantically enriched text representations in different Text Mining problems, such as sentiment analysis, recommendation systems, fake news detection, literature-based discovery and event mining. (AU)
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