Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Sobre
Download foto HD

Sobre

Sérgio Nunes é Professor Auxiliar do Departamento de Engenharia Informática da FEUP, Universidade do Porto e Investigador Sénior do INESC TEC. É Doutorado em Engenharia Informática (2010), na área da Recuperação de Informação, com trabalho focado no uso de caraterísticas temporais para estimar a relevância de informação. É Mestre em Gestão da Informação (2004) com trabalho desenvolvido na área da interoperabilidade entre sistemas de informação académicos.

Foi Diretor do U.Porto Media Innovation Labs (MIL), o Centro de Competências da Universidade do Porto com o objetivo de desenvolver a capacidade da universidade na área dos Media nas vertentes do ensino, investigação e inovação, promovendo colaborações entre as estruturas existentes e a articulação com parceiros externos.

Tem como principais interesses de investigação a Recuperação de Informações e os Sistemas de Informação na Web. No ensino, o foco são as áreas das bases de dados, das tecnologias da web e da recuperação de informação, com a coordenação de diversas unidades curriculares em diferentes programas, nomeadamente o Programa Doutoral em Engenharia Informática, o Mestrado em Engenharia Informática e o Mestrado em Multimédia.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Sérgio Nunes
  • Cluster

    Informática
  • Cargo

    Responsável de Área
  • Desde

    20 dezembro 2010
004
Publicações

2021

Managing research the wiki way

Autores
Devezas, JL; Nunes, S;

Publicação
XRDS

Abstract

2021

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

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

Publicação
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.

2020

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

Autores
Devezas, JL; Nunes, S;

Publicação
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

Autores
Devezas, J; Nunes, S;

Publicação
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).

2020

ECIR 2020 Workshops: Assessing the Impact of Going Online

Autores
Nunes, S; Little, S; Bhatia, S; Boratto, L; Cabanac, G; Campos, R; Couto, FM; Faralli, S; Frommholz, I; Jatowt, A; Jorge, A; Marras, M; Mayr, P; Stilo, G;

Publicação
CoRR

Abstract

Teses
supervisionadas

2020

Designing User Interaction with Linked Data in Historical Archives

Autor
Cláudia Raquel Amaral Conde Guedes

Instituição
UP-FEUP

2020

Graph-Based Entity-Oriented Search

Autor
José Luís da Silva Devezas

Instituição
UP-FEUP

2020

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

Autor
João Paulo Madureira Damas

Instituição
UP-FEUP

2020

Guidelines to introduce Internet voting in Portuguese elections based on the Estonian case stuty

Autor
Marlon Vinícius Andrade de Luna Freire

Instituição
UP-FEUP

2020

Evaluation of user interfaces for interaction with linked data in historical archives

Autor
Diogo Almeida Cunha

Instituição
UP-FEUP