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About

About

Ricardo Campos is a Professor at the Universidade da Beira Interior (UBI) and lecturer at the Porto Business School (PBS). He is a senior researcher of LIAAD-INESC TEC, the Artificial Intelligence and Decision Support Lab of U. Porto, and a collaborator of Ci2.ipt, the Smart Cities Research Center of the Polytechnic Institute of Tomar. He is PhD in Computer Science by the University of Porto (U. Porto), being also a former student of the Universidade da Beira Interior (UBI). He has more than 10 years of experience in Information Retrieval (IR) and Natural Language Processing (NLP), period during which his research has been recognized with multiple awards in international conferences and scientific competitions. He is the leading author of the highly impactful YAKE! keyword extractor toolkit, of the Tell me Stories project and of the Arquivo Público, among other software. His current research focuses on developing methods concerned the process of narrative extraction from texts. He has participated in several research projects and is particularly interested in practical approaches regarding the relationship behind entities, events and temporal aspects, as a means to make sense of unstructured data. He is an editorial board member of the International Journal of Data Science and Analytics (Springer) and of the Information Processing and Management Journal (Elsevier), co-chaired international conferences and workshops, and is a regular member of the scientific committee of several international conferences. He is also a member of the Scientific Advisory Forum of the Portulan Clarin - Research Infrastructure for the Science and Technology of Language. For more info please click here.

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Details

Details

  • Name

    Ricardo Campos
  • Role

    Senior Researcher
  • Since

    01st July 2012
002
Publications

2024

Pre-trained language models: What do they know?

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

Publication
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

Indexing Portuguese NLP Resources with PT-Pump-Up

Authors
Almeida, R; Campos, R; Jorge, A; Nunes, S;

Publication
CoRR

Abstract

2024

<i>Physio</i>: 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