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Publicações

Publicações por José Luís Devezas

2016

Exploring a Large News Collection Using Visualization Tools

Autores
Devezas, T; Devezas, JL; Nunes, S;

Publicação
Proceedings of the First International Workshop on Recent Trends in News Information Retrieval co-located with 38th European Conference on Information Retrieval (ECIR 2016), Padua, Italy, March 20, 2016.

Abstract
The overwhelming amount of news content published online every day has made it increasingly difficult to perform macro-level analysis of the news landscape. Visual exploration tools harness both computing power and human perception to assist in making sense of large data collections. In this paper, we employed three visualization tools to explore a dataset comprising one million articles published by news organizations and blogs. The visual analysis of the dataset revealed that 1) news and blog sources evaluate very differently the importance of similar events, granting them distinct amounts of coverage, 2) there are both dissimilarities and overlaps in the publication patterns of the two source types, and 3) the content's direction and diversity behave differently over time. Copyright © 2016 for the individual papers by the paper's authors.

2013

Creating and analysing a social network built from clips of online news

Autores
Figueira, Á; Devezas, J; Cravino, N; Revilla, LF;

Publicação
Information Systems and Technology for Organizations in a Networked Society

Abstract
Current online news media are increasingly depending on the participation of readers in their websites while readers increasingly use more sophisticated technology to access online news. In this context, the authors present the Breadcrumbs system and project that aims to provide news readers with tools to collect online news, to create a personal digital library (PDL) of clips taken from news, and to navigate not only on the own PDL, but also on external PDLs that relate to the first one. In this article, the authors present and describe the system and its paradigm for accessing news. We complement the description with the results from several tests which confirm the validity of our approach for clustering of news and for analysing the gathered data.

2013

Juggle: large-scale discovery in music recommendation

Autores
Coelho, F; Devezas, JL; Ribeiro, C;

Publicação
Open research Areas in Information Retrieval, OAIR '13, Lisbon, Portugal, May 15-17, 2013

Abstract

2013

The community structure of a multidimensional network of news clips

Autores
Devezas, JL; Figueira, AR;

Publicação
International Journal of Web Based Communities

Abstract
We analysed the community structure of a network of news clips where relationships were established by the co-reference of entities in pairs of clips. Community detection was applied to a unidimensional version of the news clips network, as well as to a multidimensional version where dimensions were defined based on three different classes of entities: places, people, and dates. The goal was to study the impact on the quality of the identified community structure when using multiple dimensions to model the network. We did a two-fold evaluation, first based on the modularity metric and then based on human input regarding community semantics. We verified that the assessments of the evaluators differed from the results provided by the modularity metric, pointing towards the relevance of the utility and network integration phases in the identification of semantically cohesive groups of news clips. Copyright © 2013 Inderscience Enterprises Ltd.

2016

Index-Based Semantic Tagging for Efficient Query Interpretation

Autores
Devezas, J; Nunes, S;

Publicação
EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION, CLEF 2016

Abstract
Modern search engines are evolving beyond ad hoc document retrieval. Nowadays, the information needs of the users can be directly satisfied through entity-oriented search, by ranking the entities or attributes that better relate to the query, as opposed to the documents that contain the best matching terms. One of the challenges in entity-oriented search is efficient query interpretation. In particular, the task of semantic tagging, for the identification of entity types in query parts, is central to understanding user intent. We compare two approaches for semantic tagging, within a single domain, one based on a Sesame triple store and another one based on a Lucene index. This provides a segmentation and annotation of the query based on the most probable entity types, leading to query classification and its subsequent interpretation. We evaluate the run time performance for the two strategies and find that there is a statistically significant speedup, of at least four times, for the index-based strategy over the triple store strategy.

2013

Temporal visualization of a multidimensional network of news clips

Autores
Gomes, F; Devezas, J; Figueira, A;

Publicação
Advances in Intelligent Systems and Computing

Abstract
The exploration of large networks carries inherent challenges in the visualization of a great amount of data. We built an interactive visualization system for the purpose of exploring a large multidimensional network of news clips over time. These are clips gathered by users from web news sources and references to people or places are extracted from. In this paper, we present the system's capabilities and user interface and discuss its advantages in terms of the browsing and extraction of knowledge from the data. These capabilities include a textual search and associated event detection, and temporal navigation allowing the user to seek a certain date and timespan. © 2013 Springer-Verlag.

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