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de interesse
Detalhes

Detalhes

  • Nome

    Carlos Manuel Soares
  • Cluster

    Informática
  • Desde

    01 janeiro 2008
007
Publicações

2021

Novelty Detection in Physical Activity

Autores
Leite, B; Abdalrahman, A; Castro, J; Frade, J; Moreira, J; Soares, C;

Publicação
Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Volume 2, Online Streaming, February 4-6, 2021.

Abstract

2020

Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks

Autores
de Sa, CR; Shekar, AK; Ferreira, H; Soares, C;

Publicação
Advances in Intelligent Systems and Computing - 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019)

Abstract

2020

Process discovery on geolocation data

Autores
Ribeiro, J; Fontes, T; Soares, C; Borges, JL;

Publicação
Transportation Research Procedia

Abstract

2020

Factual Question Generation for the Portuguese Language

Autores
Leite, B; Cardoso, HL; Reis, LP; Soares, C;

Publicação
International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2020, Novi Sad, Serbia, August 24-26, 2020

Abstract
Artificial Intelligence (AI) has seen numerous applications in the area of Education. Through the use of educational technologies such as Intelligent Tutoring Systems (ITS), learning possibilities have increased significantly. One of the main challenges for the widespread use of ITS is the ability to automatically generate questions. Bearing in mind that the act of questioning has been shown to improve the students learning outcomes, Automatic Question Generation (AQG) has proven to be one of the most important applications for optimizing this process. We present a tool for generating factual questions in Portuguese by proposing three distinct approaches. The first one performs a syntax-based analysis of a given text by using the information obtained from Part-of-speech tagging (PoS) and Named Entity Recognition (NER). The second approach carries out a semantic analysis of the sentences, through Semantic Role Labeling (SRL). The last method extracts the inherent dependencies within sentences using Dependency Parsing. All of these methods are possible thanks to Natural Language Processing (NLP) techniques. For evaluation, we have elaborated a pilot test that was answered by Portuguese teachers. The results verify the potential of these different approaches, opening up the possibility to use them in a teaching environment. © 2020 IEEE.

2020

Fraunhofer AICOS at CLEF eHealth 2020 Task 1: Clinical Code Extraction From Textual Data Using Fine-Tuned BERT Models

Autores
Costa, J; Lopes, I; Carreiro, A; Ribeiro, D; Soares, C;

Publicação
Working Notes of CLEF 2020 - Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 22-25, 2020.

Abstract

Teses
supervisionadas

2019

A Supervised Approach to Detect Bias in News Sources

Autor
Alexandre Marques de Castro Ribeiro

Instituição
UP-FEUP

2019

Dataset morphing to analyze the performance of recommender systems

Autor
André Gomes Ferreira Araújo Correia

Instituição
UP-FEUP

2019

Automatic Interpretation of Promotional Leaflets in Retail for Pricing Strategy

Autor
António Maria Aires Pereira Teixeira de Melo

Instituição
UP-FEUP

2019

Automated Feature Engineering for Classification Problems

Autor
Guilherme Felipe do Nascimento Reis

Instituição
UP-FEUP

2019

Learning to Rank with Random Forest: A Case Study in Hostel Reservations

Autor
Carolina Macedo Moreira

Instituição
UP-FEUP