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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. He is also one of the Guest Editors of the special issue on “Time and Information Retrieval” and on "Narrative Extraction from Texts" hosted by IPM Journal, being also a co-author of the "Survey of Temporal Information Retrieval and Related Applications" published by the ACM Computing Surveys (CSUR).

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

2020

YAKE! Keyword extraction from single documents using multiple local features

Authors
Campos, R; Mangaravite, V; Pasquali, A; Jorge, A; Nunes, C; Jatowt, A;

Publication
Information Sciences

Abstract
As the amount of generated information grows, reading and summarizing texts of large collections turns into a challenging task. Many documents do not come with descriptive terms, thus requiring humans to generate keywords on-the-fly. The need to automate this kind of task demands the development of keyword extraction systems with the ability to automatically identify keywords within the text. One approach is to resort to machine-learning algorithms. These, however, depend on large annotated text corpora, which are not always available. An alternative solution is to consider an unsupervised approach. In this article, we describe YAKE!, a light-weight unsupervised automatic keyword extraction method which rests on statistical text features extracted from single documents to select the most relevant keywords of a text. Our system does not need to be trained on a particular set of documents, nor does it depend on dictionaries, external corpora, text size, language, or domain. To demonstrate the merits and significance of YAKE!, we compare it against ten state-of-the-art unsupervised approaches and one supervised method. Experimental results carried out on top of twenty datasets show that YAKE! significantly outperforms other unsupervised methods on texts of different sizes, languages, and domains. © 2019 Elsevier Inc.

2020

The 3$$^{\mathrm {rd}}$$ International Workshop on Narrative Extraction from Texts: Text2Story 2020

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

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2020

Proceedings of Text2Story - Third Workshop on Narrative Extraction From Texts co-located with 42nd European Conference on Information Retrieval, Text2Story@ECIR 2020, Lisbon, Portugal, April 14th, 2020 [online only]

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

Publication
Text2Story@ECIR

Abstract

2020

Event-Related Query Classification with Deep Neural Networks

Authors
Gandhi, S; Mansouri, B; Campos, R; Jatowt, A;

Publication
The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Abstract
Users tend to search over the Internet to get the most updated news when an event occurs. Search engines should then be capable of effectively retrieving relevant documents for event-related queries. As the previous studies have shown, different retrieval models are needed for different types of events. Therefore, the first step for improving effectiveness is identifying the event-related queries and determining their types. In this paper, we propose a novel model based on deep neural networks to classify event-related queries into four categories: periodic, aperiodic, one-time-only, and non-event. The proposed model combines recurrent neural networks (by feeding two LSTM layers with query frequencies) and visual recognition models (by transforming time-series data from a 1D signal to a 2D image - later passed to a CNN model) for effective query type estimation. Worth noting is that our method uses only the time-series data of query frequencies, without the need to resort to any external sources such as contextual data, which makes it language and domain-independent with regards to the query issued. For evaluation, we build upon the previous datasets on event-related queries to create a new dataset that fits the purpose of our experiments. The obtained results show that our proposed model can achieve an F1-score of 0.87. © 2020 ACM.

2020

Joint event extraction along shortest dependency paths using graph convolutional networks

Authors
Balali, A; Asadpour, M; Campos, R; Jatowt, A;

Publication
Knowledge-Based Systems

Abstract