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About

I am an associate professor at the Department of Computer Science of the Faculty of Science of the University of Porto and the coordinator of LIAAD , the Artificial Intelligence and Decision Support Lab of UP. LIAAD is a unit of INESC TEC (Laboratório Associado) since 2007. I am a PhD in Computer Science by U. Porto, MSc. on Foundations of Advanced Information Technology by the Imperial Collegeand BSc. in Applied Maths and Computer Science, currently Computer Science (U. Porto). My research interests are Data Mining and Machine Learning, in particular association rules, web  and text intelligence and data mining for decision support. My past research also includes Inductive Logic Programming and Collaborative Data Mining. I lecture courses related to programming, information processing, data mining, and other areas of computing. While at the Faculty of Economics, where I stayed from 1996 to 2009, I launched, with other colleagues, the MSc. on Data Analysis and Decisison Support Systems, which I coordinated from 2000 to April 2008. I lead research projects on data mining and web intelligence. I was the director of the Masters in Computer Science at DCC-FCUP from June 2010 to August 2013. I co-chaired international conferences (ECML/PKD 2015, Discovery Science 2009, ECML/PKDD 05 and EPIA 01), workshops and seminars in data mining and artificial intelligence. I was Vice-President of APPIA the Portuguese Association for Artificial Intelligence.

Interest
Topics
Details

Details

  • Name

    Alípio Jorge
  • Cluster

    Computer Science
  • Role

    Centre Coordinator
  • Since

    01st January 2008
016
Publications

2020

YAKE! Keyword extraction from single documents using multiple local features

Authors
Campos, R; Mangaravite, V; Pasquali, A; Jorge, A; Nunes, C; Jatowt, A;

Publication
Information Sciences

Abstract
As the amount of generated information grows, reading and summarizing texts of large collections turns into a challenging task. Many documents do not come with descriptive terms, thus requiring humans to generate keywords on-the-fly. The need to automate this kind of task demands the development of keyword extraction systems with the ability to automatically identify keywords within the text. One approach is to resort to machine-learning algorithms. These, however, depend on large annotated text corpora, which are not always available. An alternative solution is to consider an unsupervised approach. In this article, we describe YAKE!, a light-weight unsupervised automatic keyword extraction method which rests on statistical text features extracted from single documents to select the most relevant keywords of a text. Our system does not need to be trained on a particular set of documents, nor does it depend on dictionaries, external corpora, text size, language, or domain. To demonstrate the merits and significance of YAKE!, we compare it against ten state-of-the-art unsupervised approaches and one supervised method. Experimental results carried out on top of twenty datasets show that YAKE! significantly outperforms other unsupervised methods on texts of different sizes, languages, and domains. © 2019 Elsevier Inc.

2019

Impact of Genealogical Features in Transthyretin Familial Amyloid Polyneuropathy Age of Onset Prediction

Authors
Pedroto, M; Jorge, A; Mendes Moreira, J; Coelho, T;

Publication
Practical Applications of Computational Biology and Bioinformatics, 12th International Conference, PACBB 2018, Toledo, Spain, 20-22 May, 2018.

Abstract

2019

The 2nd International Workshop on Narrative Extraction from Text: Text2Story 2019

Authors
Jorge, AM; Campos, R; Jatowt, A; Bhatia, S;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2019

Guest Editorial: Special Issue on Data Mining for Geosciences

Authors
Jorge, A; Lopes, RL; Larrazabal, G; Nikhalat Jahromi, H;

Publication
Data Mining and Knowledge Discovery

Abstract

2019

Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features

Authors
Nogueira, DM; Ferreira, CA; Gomes, EF; Jorge, AM;

Publication
Journal of Medical Systems

Abstract

Supervised
thesis

2017

Automatic Coherence Evaluation Applied to Topic Models

Author
Arian Rodrigo Pasquali

Institution
UP-FCUP

2017

APP Consumer Response: A.I. Modelling Towards Optimal Managerial Decisions in Mobile Marketing.

Author
Fabiane Valéria de Oliveira Bastos Valente

Institution
UP-FCUP

2017

Workflow Recommendation for Text Classification Problems

Author
Maria João Fernandes Ferreira

Institution
UP-FCUP

2017

Identificação de termos relevantes em relatórios usando text mining

Author
Pedro da Silva Bastos

Institution
UP-FCUP

2016

Technopolitcs and 2013 popular uprisings in Brasil: a data analysis study on networked emotional contagious and political mobilization

Author
Marcela Canavarro Rodrigues Martins

Institution
UP-FCUP