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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

    Affiliated Researcher
  • Since

    01st January 2010
002
Publications

2020

Incremental Approach for Automatic Generation of Domain-Specific Sentiment Lexicon

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

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2020

Sentence Compression for Portuguese

Authors
Asevedo Nóbrega, FA; Jorge, AM; Brazdil, P; Pardo, TAS;

Publication
Computational Processing of the Portuguese Language - 14th International Conference, PROPOR 2020, Evora, Portugal, March 2-4, 2020, Proceedings

Abstract
The task of Sentence Compression aims at producing a shorter version of a given sentence. This task may assist many other applications, as Automatic Summarization and Text Simplification. In this paper, we investigate methods for Sentence Compression for Portuguese. We focus on machine learning-based algorithms and propose new strategies. We also create reference corpora/datasets for the area, allowing to train and to test the methods of interest. Our results show that some of our methods outperform previous initiatives for Portuguese and produce competitive results with a state of the art method in the area. © Springer Nature Switzerland AG 2020.

2019

Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks

Authors
Cordeiro, M; Sarmento, RP; Brazdil, P; Kimura, M; Gama, J;

Publication
Complex Networks and Their Applications VIII - Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, Lisbon, Portugal, December 10-12, 2019.

Abstract
Discovering communities in a network is a fundamental and important problem to complex networks. Find the most influential actors among its peers is a major task. If on one side, studies on community detection ignore the influence of actors and communities, on the other hand, ignoring the hierarchy and community structure of the network neglect the actor or community influence. We bridge this gap by combining a dynamic community detection method with a dynamic centrality measure. The proposed enhanced dynamic hierarchical community detection method computes centrality for nodes and aggregated communities and selects each community representative leader using the ranked centrality of every node belonging to the community. This method is then able to unveil, track, and measure the importance of main actors, network intra and inter-community structural hierarchies based on a centrality measure. The empirical analysis performed, using two temporal networks shown that the method is able to find and tracking community leaders in evolving networks. © 2020, Springer Nature Switzerland AG.

2019

Association and temporality between news and tweets

Authors
Moutinho, V; Brazdil, P; Cordeiro, J;

Publication
IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Abstract
With the advent of social media, the boundaries of mainstream journalism and social networks are becoming blurred. User-generated content is increasing, and hence, journalists dedicate considerable time searching platforms such as Facebook and Twitter to announce, spread, and monitor news and crowd check information. Many studies have looked at social networks as news sources, but the relationship and interconnections between this type of platform and news media have not been thoroughly investigated. In this work, we have studied a series of news articles and examined a set of related comments on a social network during a period of six months. Specifically, a sample of articles from generalist Portuguese news sources published on the first semester of 2016 was clustered, and the resulting clusters were then associated with tweets of Portuguese users with the recourse to a similarity measure. Focusing on a subset of clusters, we have performed a temporal analysis by examining the evolution of the two types of documents (articles and tweets) and the timing of when they appeared. It appears that for some stories, namely Brexit and the European Football Cup, the publishing of news articles intensifies on key dates (event-oriented), while the discussion on social media is more balanced throughout the months leading up to those events. Copyright

2019

Simplifying the Algorithm Selection Using Reduction of Rankings of Classification Algorithms

Authors
Abdulrahman, SM; Brazdil, P; Zainon, WMNW; Adamu, A;

Publication
2019 8TH INTERNATIONAL CONFERENCE ON SOFTWARE AND COMPUTER APPLICATIONS (ICSCA 2019)

Abstract
The average ranking method (AR) is one of the simplest and effective algorithms selection methods. This method uses metadata in the form of test results of a given set of algorithms on a given set of datasets and calculates an average rank for each algorithm. The ranks are used to construct the average ranking. In this paper we investigate the problem of how the rankings can be reduced by removing non-competitive and redundant algorithms, thereby reducing the number of tests a user needs to conduct on a new dataset to identify the most suitable algorithm. The method proposed involves two phases. In the first one, the aim is to identify the most competitive algorithms for each dataset used in the past. This is done with the recourse to a statistical test. The second phase involves a covering method whose aim is to reduce the algorithms by eliminating redundant variants. The proposed method differs from one earlier proposal in various aspects. One important one is that it takes both accuracy and time into consideration. The proposed method was compared to the baseline strategy which consists of executing all algorithms from the ranking. It is shown that the proposed method leads to much better performance than the baseline.

Supervised
thesis

2017

Automatic Recommendation of Machine Learning Workflows

Author
Miguel Alexandre Viana Cachada

Institution
UP-FEP

2017

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

Author
André Martinez Candeias Lima

Institution
UP-FEP

2017

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

Author
Salisu Mamman Abdulrahman

Institution
UP-FEP

2017

Workflow Recommendation for Text Classification Problems

Author
Maria João Fernandes Ferreira

Institution
UP-FCUP

2015

Sentiment Analysis in Financial News

Author
Patrícia Alexandra Lopes Antunes

Institution
UP-FEP