Sobre
Áreas de Interesse:
- Recuperação de Informação
- Ciência das Redes
- Ciência da Informação
- Aprendizagem Máquina
- Tecnologias Web
Áreas de Interesse: Recuperação de Informação Ciência das Redes Ciência da Informação Aprendizagem Máquina Tecnologias Web
Áreas de Interesse:
2020
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.
2019
Autores
Devezas, J; Nunes, S;
Publicação
Open Computer Science
Abstract
2019
Autores
Devezas, JL; Lopes, CT; Nunes, S;
Publicação
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.
2019
Autores
Devezas, JL; Nunes, S;
Publicação
Complex Networks and Their Applications VIII - Volume 2 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, Lisbon, Portugal, December 10-12, 2019.
Abstract
2018
Autores
Devezas, JL; Nunes, S;
Publicação
Proceedings of the Second International Workshop on Recent Trends in News Information Retrieval co-located with 40th European Conference on Information Retrieval (ECIR 2018), Grenoble, France, March 26, 2018.
Abstract
Social media platforms are having a profound impact on the so-called information ecosystem, specifically on how information is produced, distributed and consumed. Social media in particular has contributed to the rise of user generated content and consequently to a greater diversity in online content. On the other hand, social media networks, such as Twitter or Facebook, have become information management tools that allow users to setup and configure information sources to their particular interests. A Twitter user can handpick the sources he wishes to follow, thus creating a custom information channel. However, this opportunity to create personalized information channels effectively results in different consumption profiles? Is the information consumed by users through social media networks distinct from the information consumed though traditional mainstream media? In this work, we set out to investigate this question using Twitter as a case study. We prepare two samples of users, one based on a uniform random selection of user IDs, and another one based on a selection of mainstream media followers. We analyze the home timelines of the users in each sample, focusing on characterizing information consumption habits. We find that information consumption volume is higher, while diversity is consistently lower, for mainstream media followers when compared to random users. When analyzing daily behavior, however, the samples slightly approximate, while clearly maintaining a lower diversity for mainstream media followers and a higher diversity for random users. Copyright © 2018 for the individual papers by the papers’ authors.
Teses supervisionadas
2017
Autor
Inês Davim Lopes Garganta Silva
Instituição
UP-FEUP
2017
Autor
André Ricardo Oliveira Pires
Instituição
UP-FEUP
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