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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 Computer Science Masters, and the Communication Science Bachelors.

Interest
Topics
Details

Details

  • Name

    Sérgio Nunes
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    20th December 2010
003
Publications

2019

Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?

Authors
Fortuna, P; Company, JS; Nunes, S;

Publication
Proceedings of the 13th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2019, Minneapolis, MN, USA, June 6-7, 2019

Abstract

2019

Hypergraph-of-entity

Authors
Devezas, J; Nunes, S;

Publication
Open Computer Science

Abstract
AbstractModern search is heavily powered by knowledge bases, but users still query using keywords or natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We have previously proposed the graph-of-entity as a purely graph-based representation and retrieval model, however this model would scale poorly. We tackle the scalability issue by adapting the model so that it can be represented as a hypergraph. This enables a significant reduction of the number of (hyper)edges, in regard to the number of nodes, while nearly capturing the same amount of information. Moreover, such a higher-order data structure, presents the ability to capture richer types of relations, including nary connections such as synonymy, or subsumption. We present the hypergraph-of-entity as the next step in the graph-of-entity model, where we explore a ranking approach based on biased random walks. We evaluate the approaches using a subset of the INEX 2009 Wikipedia Collection. While performance is still below the state of the art, we were, in part, able to achieve a MAP score similar to TF-IDF and greatly improve indexing efficiency over the graph-of-entity.

2019

Information Processing & Management Journal Special Issue on Narrative Extraction from Texts (Text2Story): Preface

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

Publication
Information Processing and Management

Abstract

2019

Graph-of-entity: A model for combined data representation and retrieval

Authors
Devezas, JL; Lopes, CT; Nunes, S;

Publication
OpenAccess Series in Informatics

Abstract
Managing large volumes of digital documents along with the information they contain, or are associated with, can be challenging. As systems become more intelligent, it increasingly makes sense to power retrieval through all available data, where every lead makes it easier to reach relevant documents or entities. Modern search is heavily powered by structured knowledge, but users still query using keywords or, at the very best, telegraphic natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We tackle entity-oriented search using graph-based approaches for representation and retrieval. In particular, we propose the graph-of-entity, a novel approach for indexing combined data, where terms, entities and their relations are jointly represented. We compare the graph-of-entity with the graph-of-word, a text-only model, verifying that, overall, it does not yet achieve a better performance, despite obtaining a higher precision. Our assessment was based on a small subset of the INEX 2009 Wikipedia Collection, created from a sample of 10 topics and respectively judged documents. The offline evaluation we do here is complementary to its counterpart from TREC 2017 OpenSearch track, where, during our participation, we had assessed graph-of-entity in an online setting, through team-draft interleaving. © José Devezas, Carla Lopes, and Sérgio Nunes.

2018

Proceedings of the First Workshop on Narrative Extraction From Text (Text2Story 2018) co-located with 40th European Conference on Information Retrieval (ECIR 2018), Grenoble, France, March 26, 2018

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

Publication
Text2Story@ECIR

Abstract

Supervised
thesis

2017

Estudo e caraterização dos hábitos de utilização e navegação em jornais online

Author
João Miguel Falcão Morgado

Institution
UP-FEUP

2017

Statistical Language Models applied to News Generation

Author
João Ricardo Pintas Soares

Institution
UP-FEUP

2017

Automatic detection of hate speech in text: an overview of the topic and dataset annotation with hierarchical classes

Author
Paula Cristina Teixeira Fortuna

Institution
UP-FEUP

2017

Designing an Algorithmic News Application for User Control

Author
Tiago Nuno Mesquita Folgado Leitão Devezas

Institution
UP-FEUP

2017

Graph-Based Entity-Oriented Search

Author
José Luís da Silva Devezas

Institution
UP-FEUP