
Ricardo Campos
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.
Projects
Text2Story
Nowadays, journalistic content is distributed in multiple formats, mostly through the web and specific internet-based applications running on smartphones and tablets. Text is a very important format, but readers (or more accurately users or information consumers) heavily rely on images, videos, slideshows, charts and infographics. Textual content is still the main representation for information. Any journalistic subject (e.g. Trump and Russia) is described in one or more texts produced by journalists and possibly commented by readers. Many of those subjects are followed during days, weeks or months. To grasp a possibly vast and somewhat complex set of interconnected news articles, readers would greatly benefit from tools that summarize those articles by showing main actors, their interplay and their trajectories in time and space, their motivations, main events, causal relations of events and outcomes. In the Text2Story project we use Artificial Intelligence, Computer Science and Linguistics to automatically extract those narrative elements using a well-defined semantic framework and re-represent them in formats that convey the essential story but that are more efficiently consumed by the users. The project is lead by INESC TEC in collaboration with researchers from the Center of Linguistics of U. Porto, the Lusa News Agency and Jornal Público.
PTPumpup
Publications
Special issue on selected papers from ICADL 2022
Jatowt, A;Katsurai, M;Pozi, MSM;Campos, R;
2024
INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
ACE-2005-PT: Corpus for Event Extraction in Portuguese
Cunha, LF;Silvano, P;Campos, R;Jorge, A;
2024
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024
Keywords attention for fake news detection using few positive labels
de Souza, MC;Golo, MPS;Jorge, AMG;de Amorim, ECF;Campos, RNT;Marcacini, RM;Rezende, SO;
2024
INFORMATION SCIENCES
Preface
Campos, R;Jorge, AM;Jatowt, A;Bhatia, S;Rocha, C;Cordeiro, JP;
2020
CEUR Workshop Proceedings
Supervised theses
Robust Detection and Tracking of Players and Ball in Padel Matches: A Computer Vision Approach
João Pedro Fontes Vilhena e Mascarenhas
M - 2024
UP-FEUP