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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

Alípio Jorge
• #### Cluster

Computer Science
• #### Role

Centre Coordinator
• #### Since

01st January 2008
019
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.

2020

### The 3$$^{\mathrm {rd}}$$ International Workshop on Narrative Extraction from Texts: Text2Story 2020

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

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2020

### Proceedings of Text2Story - Third Workshop on Narrative Extraction From Texts co-located with 42nd European Conference on Information Retrieval, Text2Story@ECIR 2020, Lisbon, Portugal, April 14th, 2020 [online only]

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

Publication
Text2Story@ECIR

Abstract

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

Authors
Loureiro, D; Jorge, AM;

Publication
Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part II

Abstract

Supervised
thesis

2019

### Deep learning approach to customer feedback understanding

Author
Ricardo Garcia Oliveira

Institution
UP-FCUP

2019

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

Author
Fabiane Valéria de Oliveira Bastos Valente

Institution
UP-FEP

2019

### Natural Language Inference using Relational Commonsense Knowledge

Author
Daniel Alexandre Bouçanova Loureiro

Institution
UP-FCUP

2019

### Time-To-Event Prediction

Author
Maria José Gomes Pedroto

Institution
UP-FEUP

2019

### Political mobilization in Brazil from 2013 to 2017: a technopolitical analysis using surveys and social network data mining

Author
Marcela Canavarro Rodrigues Martins

Institution
UP-FEUP