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About

About

Pavel Brazdil is a founder of a strong Machine Learning / Data Mining group that exists since 1988 and which now is a part of LIAAD Inesc Tec (Laboratory of AI and Decision Support). Pavel Brazdil is Full Professor (Prof. Catedrático) at the Faculty of Economics (FEP) of University of Porto, where he has been teaching courses on Information systems, Data Mining and Text Mining. He has supervised 12 PhD students. Although he has officially retired in mid-July 2015, he continues his R&D activities, including teaching at Master and Doctoral courses and supervision of post-graduate students.

Interest
Topics
Details

Details

  • Name

    Pavel Brazdil
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st January 2010
001
Publications

2023

AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages

Authors
Muhammad, SH; Abdulmumin, I; Ayele, AA; Ousidhoum, N; Adelani, DI; Yimam, SM; Ahmad, IS; Beloucif, M; Mohammad, S; Ruder, S; Hourrane, O; Brazdil, P; António Ali, FDM; David, D; Osei, S; Bello, BS; Ibrahim, F; Gwadabe, T; Rutunda, S; Belay, TD; Messelle, WB; Balcha, HB; Chala, SA; Gebremichael, HT; Opoku, B; Arthur, S;

Publication
CoRR

Abstract

2023

Exploring the Reduction of Configuration Spaces of Workflows

Authors
Freitas, F; Brazdil, P; Soares, C;

Publication
Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings

Abstract
Many current AutoML platforms include a very large space of alternatives (the configuration space) that make it difficult to identify the best alternative for a given dataset. In this paper we explore a method that can reduce a large configuration space to a significantly smaller one and so help to reduce the search time for the potentially best workflow. We empirically validate the method on a set of workflows that include four ML algorithms (SVM, RF, LogR and LD) with different sets of hyperparameters. Our results show that it is possible to reduce the given space by more than one order of magnitude, from a few thousands to tens of workflows, while the risk that the best workflow is eliminated is nearly zero. The system after reduction is about one order of magnitude faster than the original one, but still maintains the same predictive accuracy and loss. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

Combining Symbolic and Deep Learning Approaches for Sentiment Analysis

Authors
Muhammad, SH; Brazdil, P; Jorge, A;

Publication
Compendium of Neurosymbolic Artificial Intelligence

Abstract
Deep learning approaches have become popular in sentiment analysis because of their competitive performance. The downside of this approach is that they do not provide understandable explanations on how the sentiment values are calculated. Previous approaches that used sentiment lexicons for sentiment analysis can do that, but their performance is lower than deep learning approaches. Therefore, it is natural to wonder if the two approaches can be combined to exploit their advantages. In this chapter, we present a neuro-symbolic approach that combines both symbolic and deep learning approaches for sentiment analysis tasks. The symbolic approach exploits sentiment lexicon and shifter patterns-which cover the operations of inversion/reversal, intensification, and attenuation/downtoning. The deep learning approach used a pre-trained language model (PLM) to construct sentiment lexicon. Our experimental result shows that the proposed approach leads to promising results, substantially better than the results of a pure lexicon-based approach. Although the results did not reach the level of the deep learning approach, a great advantage is that sentiment prediction can be accompanied by understandable explanations. For some users, it is very important to see how sentiment is derived, even if performance is a little lower. © 2023 The authors and IOS Press. All rights reserved.

2023

Combining symbolic and deep learning approaches for sentiment analysis

Authors
Muhammad, SH; Brazdil, P; Jorge, A;

Publication
Frontiers in Artificial Intelligence and Applications

Abstract
Deep learning approaches have become popular in sentiment analysis because of their competitive performance. The downside of this approach is that they do not provide understandable explanations on how the sentiment values are calculated. Previous approaches that used sentiment lexicons for sentiment analysis can do that, but their performance is lower than deep learning approaches. Therefore, it is natural to wonder if the two approaches can be combined to exploit their advantages. In this chapter, we present a neuro-symbolic approach that combines both symbolic and deep learning approaches for sentiment analysis tasks. The symbolic approach exploits sentiment lexicon and shifter patterns-which cover the operations of inversion/reversal, intensification, and attenuation/downtoning. The deep learning approach used a pre-trained language model (PLM) to construct sentiment lexicon. Our experimental result shows that the proposed approach leads to promising results, substantially better than the results of a pure lexicon-based approach. Although the results did not reach the level of the deep learning approach, a great advantage is that sentiment prediction can be accompanied by understandable explanations. For some users, it is very important to see how sentiment is derived, even if performance is a little lower. © 2023 The authors and IOS Press. All rights reserved.

2022

Detection of Loanwords in Angolan Portuguese: A Text Mining Approach

Authors
Muhongo, TS; Brazdil, PB; Silva, F;

Publication
INTELIGENCIA ARTIFICIAL-IBEROAMERICAL JOURNAL OF ARTIFICIAL INTELLIGENCE

Abstract
Angola is characterized by many different languages and social, cultural and political realities, which had a marked effect on Angolan Portuguese (AP). Consequently, AP is characterized by diatopic variation. One of the marked effects is the loanwords imported from other Angolan languages. Our objective is to analyze different Angolan texts, analyze the lexical forms used and conduct a comparative study with European Portuguese, aiming at identifying the possible loanwords in Angolan Portuguese. This process was automated, as well as the identification of all loanwords' cotexts. In addition, we determine the lexical class of each loanword and the Angolan language of its origin. Most lexical loanwords come from the Kimbundu, although AP includes loanwords from some other Angolan languages too. Our study serves as a basis for preparing an Angolan regionalism dictionary. We noticed that more than 700 identified loanwords do not figure in the existing dictionaries.

Supervised
thesis

2017

Identifying Affinity Groups of Researchers in FEP through the Application of Community Detection Algorithms

Author
André Martinez Candeias Lima

Institution
UP-FEP

2017

Workflow Recommendation for Text Classification Problems

Author
Maria João Fernandes Ferreira

Institution
UP-FCUP

2017

Automatic Recommendation of Machine Learning Workflows

Author
Miguel Alexandre Viana Cachada

Institution
UP-FEP

2017

Improving Algorithm Selection Methods using Meta-Learning by Considering Accuracy and Run Time

Author
Salisu Mamman Abdulrahman

Institution
UP-FEP

2015

Development of a support system for workflow design for data mining problems that exploits Meta-learning

Author
Salisu Mamman Abdulrahman

Institution
UP-FEP