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About

Sérgio Nunes is an Assistant Professor at the Department of Informatics Engineering at FEUP, University of Porto, and a Senior Researcher at INESC TEC. He holds a PhD in Information Retrieval (2010) focused on using temporal features for relevance estimation, and a MSc in Information Management (2004).

Was the Director of the U.Porto Media Innovation Labs (MIL), an Excellence Center of the University of Porto, with the mission of developing the university's capacity in the field of Media in teaching, research and innovation activities by promoting collaborations between existing university structures and articulation with external partners.

His main research interests are in Information Retrieval and Web Information Systems. He teaches databases, web technologies and information retrieval in different programs, namely the Informatics Engineering Doctoral Program, the Informatics Engineering Masters, and the Multimedia Masters.

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Details

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004
Publications

2021

Managing research the wiki way

Authors
Devezas, JL; Nunes, S;

Publication
XRDS

Abstract

2021

Brat2Viz: a Tool and Pipeline for Visualizing Narratives from Annotated Texts

Authors
Amorim, E; Ribeiro, A; Santana, BS; Cantante, I; Jorge, A; Nunes, S; Silvano, P; Leal, A; Campos, R;

Publication
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).

Abstract
Narrative Extraction from text is a complex task that starts by identifying a set of narrative elements (actors, events, times), and the semantic links between them (temporal, referential, semantic roles). The outcome is a structure or set of structures which can then be represented graphically, thus opening room for further and alternative exploration of the plot. Such visualization can also be useful during the on-going annotation process. Manual annotation of narratives can be a complex effort and the possibility offered by the Brat annotation tool of annotating directly on the text does not seem sufficiently helpful. In this paper, we propose Brat2Viz, a tool and a pipeline that displays visualization of narrative information annotated in Brat. Brat2Viz reads the annotation file of Brat, produces an intermediate representation in the declarative language DRS (Discourse Representation Structure), and from this obtains the visualization. Currently, we make available two visualization schemes: MSC (Message Sequence Chart) and Knowledge Graphs. The modularity of the pipeline enables the future extension to new annotation sources, different annotation schemes, and alternative visualizations or representations. We illustrate the pipeline using examples from an European Portuguese news corpus. Copyright © by the paper's authors.

2021

A Review of Graph-Based Models for Entity-Oriented Search

Authors
Devezas, J; Nunes, S;

Publication
SN Computer Science

Abstract

2020

Army ANT: A workbench for innovation in entity-oriented search

Authors
Devezas, JL; Nunes, S;

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

Abstract
As entity-oriented search takes the lead in modern search, the need for increasingly flexible tools, capable of motivating innovation in information retrieval research, also becomes more evident. Army ANT is an open source framework that takes a step forward in generalizing information retrieval research, so that modern approaches can be easily integrated in a shared evaluation environment. We present an overview on the system architecture of Army ANT, which has four main abstractions: (i) readers, to iterate over text collections, potentially containing associated entities and triples; (ii) engines, that implement indexing and searching approaches, supporting different retrieval tasks and ranking functions; (iii) databases, to store additional document metadata; and (iv) evaluators, to assess retrieval performance for specific tasks and test collections. We also introduce the command line interface and the web interface, presenting a learn mode as a way to explore, analyze and understand representation and retrieval models, through tracing, score component visualization and documentation. © Springer Nature Switzerland AG 2020.

2020

Characterizing the hypergraph-of-entity and the structural impact of its extensions

Authors
Devezas, J; Nunes, S;

Publication
Applied Network Science

Abstract
The hypergraph-of-entity is a joint representation model for terms, entities and their relations, used as an indexing approach in entity-oriented search. In this work, we characterize the structure of the hypergraph, from a microscopic and macroscopic scale, as well as over time with an increasing number of documents. We use a random walk based approach to estimate shortest distances and node sampling to estimate clustering coefficients. We also propose the calculation of a general mixed hypergraph density measure based on the corresponding bipartite mixed graph. We analyze these statistics for the hypergraph-of-entity, finding that hyperedge-based node degrees are distributed as a power law, while node-based node degrees and hyperedge cardinalities are log-normally distributed. We also find that most statistics tend to converge after an initial period of accentuated growth in the number of documents. We then repeat the analysis over three extensions—materialized through synonym, context, and tf_bin hyperedges—in order to assess their structural impact in the hypergraph. Finally, we focus on the application-specific aspects of the hypergraph-of-entity, in the domain of information retrieval. We analyze the correlation between the retrieval effectiveness and the structural features of the representation model, proposing ranking and anomaly indicators, as useful guides for modifying or extending the hypergraph-of-entity. © 2020, The Author(s).

Supervised
thesis

2020

Prediction of toxicity-generating news using machine learning

Author
Luís Leão Aguiar Braga da Cruz

Institution
UP-FEUP

2020

Metrics and tools for exploring toxicity in social media

Author
Pedro Miguel Ferraz Nogueira da Silva

Institution
UP-FEUP

2020

Designing User Interaction with Linked Data in Historical Archives

Author
Cláudia Raquel Amaral Conde Guedes

Institution
UP-FEUP

2020

Graph-Based Entity-Oriented Search

Author
José Luís da Silva Devezas

Institution
UP-FEUP

2020

Building a domain-specific search engine that explores football-related search patterns

Author
João Paulo Madureira Damas

Institution
UP-FEUP