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Publicações

Publicações por José Luís Devezas

2017

Information Extraction for Event Ranking

Autores
Devezas, JL; Nunes, S;

Publicação
6th Symposium on Languages, Applications and Technologies, SLATE 2017, June 26-27, 2017, Vila do Conde, Portugal

Abstract
Search engines are evolving towards richer and stronger semantic approaches, focusing on entity-oriented tasks where knowledge bases have become fundamental. In order to support semantic search, search engines are increasingly reliant on robust information extraction systems. In fact, most modern search engines are already highly dependent on a well-curated knowledge base. Nevertheless, they still lack the ability to e ectively and automatically take advantage of multiple heterogeneous data sources. Central tasks include harnessing the information locked within textual content by linking mentioned entities to a knowledge base, or the integration of multiple knowledge bases to answer natural language questions. Combining text and knowledge bases is frequently used to improve search results, but it can also be used for the query-independent ranking of entities like events. In this work, we present a complete information extraction pipeline for the Portuguese language, covering all stages from data acquisition to knowledge base population. We also describe a practical application of the automatically extracted information, to support the ranking of upcoming events displayed in the landing page of an institutional search engine, where space is limited to only three relevant events. We manually annotate a dataset of news, covering event announcements from multiple faculties and organic units of the institution. We then use it to train and evaluate the named entity recognition module of the pipeline. We rank events by taking advantage of identified entities, as well as partOf relations, in order to compute an entity popularity score, as well as an entity click score based on implicit feedback from clicks from the institutional search engine. We then combine these two scores with the number of days to the event, obtaining a final ranking for the three most relevant upcoming events. © José Devezas and Sérgio Nunes

2017

Graph-Based Entity-Oriented Search: Imitating the Human Process of Seeking and Cross Referencing Information

Autores
Devezas, J; Nunes, S;

Publicação
ERCIM News

Abstract

2012

Studying a Personality Coreference Network in a News Stories Photo Collection

Autores
José Devezas; Filipe Coelho; Sérgio Nunes; Cristina Ribeiro

Publicação
ECIR 2012 - European Conference on Information Retrieval, Barcelona, Spain

Abstract
We build and analyze a coreference network based on entities from photo descriptions, where nodes represent personalities and edges connect people mentioned in the same photo description. We identify and characterize the communities in this network and propose taking advantage of the context provided by community detection methodologies to improve text illustration and general search.

2013

Large-scale crossmedia retrieval for playlist generation and song discovery

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

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

Abstract

2018

Social Media and Information Consumption Diversity

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.

2017

FEUP at TREC 2017 OpenSearch Track Graph-Based Models for Entity-Oriented

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

Publicação
Proceedings of The Twenty-Sixth Text REtrieval Conference, TREC 2017, Gaithersburg, Maryland, USA, November 15-17, 2017

Abstract

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