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Sobre

Sobre

Sou professor associado do Departamento de Ciência de Computadores da Faculdade de Ciências da Universidade do Porto e coordenador do LIAAD, Laboratório de Inteligência Artificial e de Apoio à Decisão da UP. O LIAAD é um cenrto do INESC TEC desde 2007. Sou doutor em Ciência da Computação pela U. Porto, MSc. em Fundamentos de Tecnologia de Informação Avançada pelo Imperial College e Lic. Em Matemática Aplicada ramo Ciência de Computadores (U. Porto). Os meus interesses de investigação são Extração de Conhecimento (Data Mining) e Aprendizagem Automática (Machine Learning), em particular regras de associação, text mining e sistemas de recomendação. A minha investigação anterior inclui programação em lógica indutiva e data miing colaborativo. Eu leciono cursos relacionados com programação, processamento de informação, data mining e outras áreas da computação. Enquanto na Faculdade de Economia, onde permaneci de 1996 a 2009, lancei, com outros colegas, o mestrado em Análise de Dados e Sistemas de Apoio à Decisão (MADSAD), que coordenei de 2000 a Abril de 2008. Dirijo projetos em data mining e inteligência na web. Fui diretor do Mestrado em Ciência dos Computadores no DCC-FCUP de junho de 2010 a agosto de 2013. Co-organizei conferências internacionais (ECML / PKD 2015, Discovery Science 2009, ECML / PKDD 05 e EPIA 01), workshops e seminários em data mining e inteligência artificial. Fui Vice-Presidente da APPIA Associação Portuguesa para a Inteligência Artificial.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Alípio Jorge
  • Cargo

    Coordenador de Centro
  • Desde

    01 janeiro 2008
020
Publicações

2024

Pre-trained language models: What do they know?

Autores
Guimaraes, N; Campos, R; Jorge, A;

Publicação
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Large language models (LLMs) have substantially pushed artificial intelligence (AI) research and applications in the last few years. They are currently able to achieve high effectiveness in different natural language processing (NLP) tasks, such as machine translation, named entity recognition, text classification, question answering, or text summarization. Recently, significant attention has been drawn to OpenAI's GPT models' capabilities and extremely accessible interface. LLMs are nowadays routinely used and studied for downstream tasks and specific applications with great success, pushing forward the state of the art in almost all of them. However, they also exhibit impressive inference capabilities when used off the shelf without further training. In this paper, we aim to study the behavior of pre-trained language models (PLMs) in some inference tasks they were not initially trained for. Therefore, we focus our attention on very recent research works related to the inference capabilities of PLMs in some selected tasks such as factual probing and common-sense reasoning. We highlight relevant achievements made by these models, as well as some of their current limitations that open opportunities for further research.This article is categorized under:Fundamental Concepts of Data and Knowledge > Key Design Issues in DataMiningTechnologies > Artificial Intelligence

2024

<i>Physio</i>: An LLM-Based Physiotherapy Advisor

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

Publicação
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

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

Publicação
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?

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

Publicação
WIREs Data. Mining. Knowl. Discov.

Abstract

2024

Keywords attention for fake news detection using few positive labels

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

Publicação
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.

Teses
supervisionadas

2023

Product Complaint Understanding using NLP Techniques

Autor
Beatriz Marques Arcipreste

Instituição
UP-FCUP

2023

Unfolding the Temporal Structure of Narratives

Autor
Hugo Miguel Oliveira de Sousa

Instituição
UP-FCUP

2023

Time-To-Event Prediction

Autor
Maria José Gomes Pedroto

Instituição
UP-FCUP

2023

Predicting user personality from digital media

Autor
Ricardo da Cunha Magalhães Lopes

Instituição
UP-FCUP

2023

Learning Word Sense Representations from Neural Language Models

Autor
Daniel Alexandre Bouçanova Loureiro

Instituição
UP-FCUP