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Documents authored by Devezas, José

Document
DOI: 10.4230/OASIcs.SLATE.2019.1
Graph-of-Entity: A Model for Combined Data Representation and Retrieval

Authors: José Devezas, Carla Lopes, and Sérgio Nunes

Published in: OASIcs, Volume 74, 8th Symposium on Languages, Applications and Technologies (SLATE 2019)

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.
Cite as

José Devezas, Carla Lopes, and Sérgio Nunes. Graph-of-Entity: A Model for Combined Data Representation and Retrieval. In 8th Symposium on Languages, Applications and Technologies (SLATE 2019). Open Access Series in Informatics (OASIcs), Volume 74, pp. 1:1-1:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{devezas_et_al:OASIcs.SLATE.2019.1,
  author =	{Devezas, Jos\'{e} and Lopes, Carla and Nunes, S\'{e}rgio},
  title =	{{Graph-of-Entity: A Model for Combined Data Representation and Retrieval}},
  booktitle =	{8th Symposium on Languages, Applications and Technologies (SLATE 2019)},
  pages =	{1:1--1:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-114-6},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{74},
  editor =	{Rodrigues, Ricardo and Janou\v{s}ek, Jan and Ferreira, Lu{\'\i}s and Coheur, Lu{\'\i}sa and Batista, Fernando and Gon\c{c}alo Oliveira, Hugo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2019.1},
  URN =		{urn:nbn:de:0030-drops-108686},
  doi =		{10.4230/OASIcs.SLATE.2019.1},
  annote =	{Keywords: Entity-oriented search, graph-based models, collection-based graph}
}
Document
DOI: 10.4230/OASIcs.SLATE.2017.18
Information Extraction for Event Ranking

Authors: José Devezas and Sérgio Nunes

Published in: OASIcs, Volume 56, 6th Symposium on Languages, Applications and Technologies (SLATE 2017)

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 effectively 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.
Cite as

José Devezas and Sérgio Nunes. Information Extraction for Event Ranking. In 6th Symposium on Languages, Applications and Technologies (SLATE 2017). Open Access Series in Informatics (OASIcs), Volume 56, pp. 18:1-18:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{devezas_et_al:OASIcs.SLATE.2017.18,
  author =	{Devezas, Jos\'{e} and Nunes, S\'{e}rgio},
  title =	{{Information Extraction for Event Ranking}},
  booktitle =	{6th Symposium on Languages, Applications and Technologies (SLATE 2017)},
  pages =	{18:1--18:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-056-9},
  ISSN =	{2190-6807},
  year =	{2017},
  volume =	{56},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Sim\~{o}es, Alberto and Leal, Jos\'{e} Paulo and Varanda, Maria Jo\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2017.18},
  URN =		{urn:nbn:de:0030-drops-79515},
  doi =		{10.4230/OASIcs.SLATE.2017.18},
  annote =	{Keywords: Named Entity Recognition, Relation Extraction, Knowledge Base Population, Entity-Based Ranking, Academic Events}
}
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