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Peñas Padilla, Anselmo

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0000-0002-7867-0149
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Peñas Padilla
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Anselmo
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2012 - 20132
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Autor: Garrido, Guillermo ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Temporally anchored relation extraction
    (2012-12-08) Garrido, Guillermo; Cabaleiro, Bernardo; Peñas Padilla, Anselmo; Rodrigo Yuste, Álvaro
    Although much work on relation extraction has aimed at obtaining static facts, many of the target relations are actually fluents, as their validity is naturally anchored to a certain time period. This paper proposes a methodological approach to temporally anchored relation extraction. Our proposal performs distant supervised learning to extract a set of relations from a natural language corpus, and anchors each of them to an interval of temporal validity, aggregating evidence from documents supporting the relation. We use a rich graphbased document-level representation to generate novel features for this task. Results show that our implementation for temporal anchoring is able to achieve a 69% of the upper bound performance imposed by the relation extraction step. Compared to the state of the art, the overall system achieves the highest precision reported.
  • Miniatura
    Publicación
    WHAD : Wikipedia historical attributes data. Historical structured data extraction and vandalism detection from the Wikipedia edit history
    (Springer Verlag (Germany), 2013-05-28) Alfonseca, Enrique; Garrido, Guillermo; Delort, Jean Yves; Peñas Padilla, Anselmo
    This paper describes the generation of temporally anchored infobox attribute data from the Wikipedia history of revisions. By mining (attribute, value) pairs from the revision history of the English Wikipedia we are able to collect a comprehensive knowledge base that contains data on how attributes change over time. When dealing with the Wikipedia edit history, vandalic and erroneous edits are a concern for data quality. We present a study of vandalism identification in Wikipedia edits that uses only features from the infoboxes, and show that we can obtain, on this dataset, an accuracy comparable to a state-of-the-art vandalism identification method that is based on the whole article. Finally, we discuss different characteristics of the extracted dataset, which we make available for further study.