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About

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
  • Role

    Centre Coordinator
  • Since

    01st January 2008
  • Nationality

    Portugal
  • Contacts

    +351220402963
    alipio.jorge@inesctec.pt
020
Publications

2024

Physio: An LLM-Based Physiotherapy Advisor

Authors
Almeida, R; Sousa, H; Cunha, LF; Guimaraes, N; Campos, R; Jorge, A;

Publication
ADVANCES IN INFORMATION RETRIEVAL, ECIR 2024, PT V

Abstract
The capabilities of the most recent language models have increased the interest in integrating them into real-world applications. However, the fact that these models generate plausible, yet incorrect text poses a constraint when considering their use in several domains. Healthcare is a prime example of a domain where text-generative trustworthiness is a hard requirement to safeguard patient well-being. In this paper, we present Physio, a chat-based application for physical rehabilitation. Physio is capable of making an initial diagnosis while citing reliable health sources to support the information provided. Furthermore, drawing upon external knowledge databases, Physio can recommend rehabilitation exercises and over-the-counter medication for symptom relief. By combining these features, Physio can leverage the power of generative models for language processing while also conditioning its response on dependable and verifiable sources. A live demo of Physio is available at https://physio.inesctec.pt.

2024

The 7th International Workshop on Narrative Extraction from Texts: Text2Story 2024

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

Publication
ADVANCES IN INFORMATION RETRIEVAL, ECIR 2024, PT V

Abstract
The Text2Story Workshop series, dedicated to Narrative Extraction from Texts, has been running successfully since 2018. Over the past six years, significant progress, largely propelled by Transformers and Large Language Models, has advanced our understanding of natural language text. Nevertheless, the representation, analysis, generation, and comprehensive identification of the different elements that compose a narrative structure remains a challenging objective. In its seventh edition, the workshop strives to consolidate a common platform and a multidisciplinary community for discussing and addressing various issues related to narrative extraction tasks. In particular, we aim to bring to the forefront the challenges involved in understanding narrative structures and integrating their representation into established frameworks, as well as in modern architectures (e.g., transformers) and AI-powered language models (e.g., chatGPT) which are now common and form the backbone of almost every IR and NLP application. Text2Story encompasses sessions covering full research papers, work-in-progress, demos, resources, position and dissemination papers, along with keynote talks. Moreover, there is dedicated space for informal discussions on methods, challenges, and the future of research in this dynamic field.

2024

Pre-trained language models: What do they know?

Authors
Guimarães, N; Campos, R; Jorge, A;

Publication
WIREs Data. Mining. Knowl. Discov.

Abstract

2024

Keywords attention for fake news detection using few positive labels

Authors
de Souza, MC; Golo, MPS; Jorge, AMG; de Amorim, ECF; Campos, RNT; Marcacini, RM; Rezende, SO;

Publication
INFORMATION SCIENCES

Abstract
Fake news detection (FND) tools are essential to increase the reliability of information in social media. FND can be approached as a machine learning classification problem so that discriminative features can be automatically extracted. However, this requires a large news set, which in turn implies a considerable amount of human experts' effort for labeling. In this paper, we explore Positive and Unlabeled Learning (PUL) to reduce the labeling cost. In particular, we improve PUL with the network-based Label Propagation (PU-LP) algorithm. PU-LP achieved competitive results in FND exploiting relations between news and terms and using few labeled fake news. We propose integrating an attention mechanism in PU-LP that can define which terms in the network are more relevant for detecting fake news. We use GNEE, a state-of-the-art algorithm based on graph attention networks. Our proposal outperforms state-of-the-art methods, improving F-1 in 2% to 10%, especially when only 10% labeled fake news are available. It is competitive with the binary baseline, even when nearly half of the data is labeled. Discrimination ability is also visualized through t-SNE. We also present an analysis of the limitations of our approach according to the type of text found in each dataset.

2023

Geovisualisation Tools for Reporting and Monitoring Transthyretin-Associated Familial Amyloid Polyneuropathy Disease

Authors
Lopo, RX; Jorge, AM; Pedroto, M;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I

Abstract
Transthyretin-associated Familial Amyloid Polyneuropathy (TTR-FAP) is a chronic fatal disease with a high incidence in Portugal. It is therefore relevant to provide professionals and citizens with a tool that enables a detailed geographical and territorial study. For this reason, we have developed an web based application that brings together techniques applied to spatial data that allow the study of the historical progression and growth of cases in patients' residential areas and areas of origin as well as an epidemic forecast. The tool enables the exploration of geographical longitudinal data at national, district and county levels. High density regions and periods can be visually identified according to parameters selected by the user. The visual evaluation of the data and its comparison across different time spans of the disease era can have an impact on more informed decision making by those working with patients to improve their quality of life, treatment or follow-up. The tool is available online for data exploration and its code is available on GitHub for adaptation to other geospatial scenarios.

Supervised
thesis

2023

Learning Word Sense Representations from Neural Language Models

Author
Daniel Alexandre Bouçanova Loureiro

Institution
UP-FCUP

2023

Predicting user personality from digital media

Author
Ricardo da Cunha Magalhães Lopes

Institution
UP-FEUP

2023

Domain-specific and Context-aware Approaches to Sentiment Analysis

Author
Shamsuddeen Hassan Muhammad

Institution
UP-FEUP

2023

Heart Sound Analysis for Cardiovascular Diseases Identification

Author
Diogo Marcelo Esterlita Nogueira

Institution
UP-FCUP

2023

Digital technology and the social monitoring of climate change

Author
Ana Sofia Cabral Cardoso

Institution
UP-FCUP