Publications

Publications by Alípio Jorge

2026

Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part IV

Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (4)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part III

Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (3)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part II

Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (2)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part I

Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (1)

Abstract

2025

FRaN-X: FRaming and Narratives-eXplorer

Authors
Muratov, A; Shaikh, HF; Jani, V; Mahmoud, T; Xie, Z; Orel, D; Singh, A; Wang, Y; Joshi, A; Iqbal, H; Hee, MS; Sahnan, D; Nikolaidis, N; Silvano, P; Dimitrov, D; Yangarber, R; Campos, R; Jorge, A; Guimarães, N; Sartori, E; Stefanovitch, N; San Martino, GD; Piskorski, J; Nakov, P;

Publication
CoRR

Abstract

2025

Human Experts vs. Large Language Models: Evaluating Annotation Scheme and Guidelines Development for Clinical Narratives

Authors
Fernandes, AL; Silvano, P; Guimarães, N; Silva, RR; Munna, TA; Cunha, LF; Leal, A; Campos, R; Jorge, A;

Publication
Proceedings of Text2Story - Eighth Workshop on Narrative Extraction From Texts held in conjunction with the 47th European Conference on Information Retrieval (ECIR 2025), Lucca, Italy, April 10, 2025.

Abstract
Electronic Health Records (EHRs) contain vast amounts of unstructured narrative text, posing challenges for organization, curation, and automated information extraction in clinical and research settings. Developing effective annotation schemes is crucial for training extraction models, yet it remains complex for both human experts and Large Language Models (LLMs). This study compares human- and LLM-generated annotation schemes and guidelines through an experimental framework. In the first phase, both a human expert and an LLM created annotation schemes based on predefined criteria. In the second phase, experienced annotators applied these schemes following the guidelines. In both cases, the results were qualitatively evaluated using Likert scales. The findings indicate that the human-generated scheme is more comprehensive, coherent, and clear compared to those produced by the LLM. These results align with previous research suggesting that while LLMs show promising performance with respect to text annotation, the same does not apply to the development of annotation schemes, and human validation remains essential to ensure accuracy and reliability. © 2025 Copyright for this paper by its authors.

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