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Detalhes

Detalhes

  • Nome

    Arian Rodrigo Pasquali
  • Cluster

    Informática
  • Cargo

    Investigador Colaborador Externo
  • Desde

    01 abril 2016
002
Publicações

2018

A Text Feature Based Automatic Keyword Extraction Method for Single Documents

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

Publicação
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2018

YAKE! Collection-Independent Automatic Keyword Extractor

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

Publicação
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2017

Transportation in Social Media: An Automatic Classifier for Travel-Related Tweets

Autores
Pereira, J; Pasquali, A; Saleiro, P; Rossetti, R;

Publicação
Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Porto, Portugal, September 5-8, 2017, Proceedings

Abstract
In the last years researchers in the field of intelligent transportation systems have made several efforts to extract valuable information from social media streams. However, collecting domain-specific data from any social media is a challenging task demanding appropriate and robust classification methods. In this work we focus on exploring geo-located tweets in order to create a travel-related tweet classifier using a combination of bag-of-words and word embeddings. The resulting classification makes possible the identification of interesting spatio-temporal relations in São Paulo and Rio de Janeiro. © Springer International Publishing AG 2017.

2016

Assessing topic discovery evaluation measures on Facebook publications of political activists in Brazil

Autores
Pasquali, A; Canavarro, M; Campos, R; Jorge, AM;

Publicação
Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering, C3S2E '16, Porto, Portugal, July 20-22, 2016

Abstract
Automatic topic detection in document collections is an important tool for various tasks. In particular, it is valuable for studying and understanding socio-political phenomena. A currently relevant example is the automatic analysis of streams of posts issued by different activist groups in the current Brazilian turmoil, through the analysis of the generated streams of texts published on the web. It is useful to determine the relative importance of the different topics identified. We can find in the literature proposals for measuring topic relevance. In this paper, we adopt two of such measures and apply them to data sets extracted from Facebook pages related to Brazilian political activism. On top of the analysis, we then carry an experimental evaluation of the human interpretability for these two measures by comparing their outcomes with the opinion of three Brazilian professionals from the field of Communication Science and media-activists. Copyright 2016 ACM.