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About

Ricardo Campos is an assistant professor at the ICT Departmental Unit of the Polytechnic Institute of Tomar  [http://www.ipt.pt] and invited professor of the Porto Business School [http://www.pbs.up.pt]. He is an integrated researcher of LIAAD-INESC TEC [https://www.inesctec.pt/en/centres/liaad], the Artificial Intelligence and Decision Support Lab of U. Porto, and a collaborator of Ci2.ipt [http://www.ci2.ipt.pt/], the Smart Cities Research Center of the Polytechnic of Tomar.

He is PhD in Computer Science by the University of Porto (U. Porto). His PhD on temporal information retrieval led him to win the Fraunhofer Portugal Challenge 2013 and to be distinguished as an “outstanding” researcher by the INESC TEC research lab.

He has over 10 years of research experience in Information Retrieval and Natural Language Processing. In 2018, he has been awarded the best short paper award at ECIR'18 for the paper entitled "A Text Feature Based Automatic Keyword Extraction Method for Single Documents" [http://yake.inesctec.pt] and the 1st prize of the Arquivo.pt Award for the project Conta-me Histórias [http://contamehistorias.pt]. In 2019 he has been awarded the Best Demo Presentation at ECIR'19 for the paper entitled “Interactive System for Automatically Generating Temporal Narratives” [http://tellmestories.pt], and the Recognized Reviewer Award for his reviews as a PC member of ECIR'19. In the same year, he has also been nominated outstanding reviewer of the NAACL-HTL'19 conference.

He is an editorial board member of the Information Processing & Management Journal (Elsevier), co-chaired international conferences and workshops, being also a program committee member of several international conferences.

More in http://www.ccc.ipt.pt/~ricardo

Interest
Topics
Details

Details

  • Name

    Ricardo Campos
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st July 2012
001
Publications

2021

Time-Matters: Temporal Unfolding of Texts

Authors
Campos, R; Duque, J; Cândido, T; Mendes, J; Dias, G; Jorge, A; Nunes, C;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2021

TLS-Covid19: A New Annotated Corpus for Timeline Summarization

Authors
Pasquali, A; Campos, R; Ribeiro, A; Santana, BS; Jorge, A; Jatowt, A;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2021

The 4th International Workshop on Narrative Extraction from Texts: Text2Story 2021

Authors
Campos, R; Jorge, A; Jatowt, A; Bhatia, S; Finlayson, MA;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2021

Proceedings of Text2Story - Fourth Workshop on Narrative Extraction From Texts held in conjunction with the 43rd European Conference on Information Retrieval (ECIR 2021), Lucca, Italy, April 1, 2021 (online event due to Covid-19 outbreak)

Authors
Campos, R; Jorge, AM; Jatowt, A; Bhatia, S; Finlayson, MA;

Publication
Text2Story@ECIR

Abstract

2021

Exploding TV Sets and Disappointing Laptops: Suggesting Interesting Content in News Archives Based on Surprise Estimation

Authors
Jatowt, A; Hung, IC; Färber, M; Campos, R; Yoshikawa, M;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Many archival collections have been recently digitized and made available to a wide public. The contained documents however tend to have limited attractiveness for ordinary users, since content may appear obsolete and uninteresting. Archival document collections can become more attractive for users if suitable content can be recommended to them. The purpose of this research is to propose a new research direction of Archival Content Suggestion to discover interesting content from long-term document archives that preserve information on society history and heritage. To realize this objective, we propose two unsupervised approaches for automatically discovering interesting sentences from news article archives. Our methods detect interesting content by comparing the information written in the past with one created in the present to make use of a surprise effect. Experiments on New York Times corpus show that our approaches effectively retrieve interesting content. © 2021, Springer Nature Switzerland AG.