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Martínez Romo, Juan

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0000-0002-6905-7051
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Martínez Romo
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Juan
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20193
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Fecha de finalización: 2019 × Tipo: artículo ×

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Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    Can deep learning techniques improve classification performance of vandalism detection in Wikipedia?
    (Elsevier, 2019) Martinez-Rico, Juan R.; Martínez Romo, Juan; Araujo Serna, M. Lourdes
    Wikipedia is a free encyclopedia created as an international collaborative project. One of its peculiarities is that any user can edit its contents almost without restrictions, what has given rise to a phenomenon known as vandalism. Vandalism is any attempt that seeks to damage the integrity of the encyclopedia deliberately. To address this problem, in recent years several automatic detection systems and associated features have been developed. This work implements one of these systems, which uses three sets of new features based on different techniques. Specifically we study the applicability of a leading technology as deep learning to the problem of vandalism detection. The first set is obtained by expanding a list of vandal terms taking advantage of the existing semantic-similarity relations in word embeddings and deep neural networks. Deep learning techniques are applied to the second set of features, specifically Stacked Denoising Autoencoders (SDA), in order to reduce the dimensionality of a bag of words model obtained from a set of edits taken from Wikipedia. The last set uses graph-based ranking algorithms to generate a list of vandal terms from a vandalism corpus extracted from Wikipedia. These three sets of new features are evaluated separately as well as together to study their complementarity, improving the results in the state of the art. The system evaluation has been carried out on a corpus extracted from Wikipedia (WP_Vandal) as well as on another called PAN-WVC-2010 that was used in a vandalism detection competition held at CLEF conference.
  • Miniatura
    Publicación
    Discovering related scientific literature beyond semantic similarity: a new co-citation approach
    (Springer, 2019-05-17) Rodríguez Prieto, Oscar; Araujo Serna, M. Lourdes; Martínez Romo, Juan
    We propose a new approach to recommend scientific literature, a domain in which the efficient organization and search of information is crucial. The proposed system relies on the hypothesis that two scientific articles are semantically related if they are co-cited more frequently than they would be by pure chance. This relationship can be quantified by the probability of co-citation, obtained from a null model that statistically defines what we consider pure chance. Looking for article pairs that minimize this probability, the system is able to recommend a ranking of articles in response to a given article. This system is included in the co-occurrence paradigm of the field. More specifically, it is based on co-cites so it can produce recommendations more focused on relatedness than on similarity. Evaluation has been performed on the ACL Anthology collection and on the DBLP dataset, and a new corpus has been compiled to evaluate the capacity of the proposal to find relationships beyond similarity. Results show that the system is able to provide, not only articles similar to the submitted one, but also articles presenting other kind of relations, thus providing diversity, i.e. connections to new topics.
  • Miniatura
    Publicación
    Deep learning approach for negation trigger and scope recognition
    (Sociedad Española para el Procesamiento del Lenguaje Natural, 2019) Fabregat Marcos, Hermenegildo; Araujo Serna, M. Lourdes; Martínez Romo, Juan
    La detección automática de los distintos elementos de la negación es un frecuente tema de estudio debido a su alto impacto en diversas tareas de procesamiento de lenguaje natural. Este articulo presenta un sistema basado en deep learning y de arquitectura no dependiente del idioma para la detección automática tanto de disparadores como del alcance de la negación para inglés y español. El sistema presentado obtiene para ingles resultados comparables a los obtenidos en recientes trabajos por sistemas más complejos. Para español destacan los resultados obtenidos en la detección de claves de negación. Por último, los resultados para el reconocimiento del alcance de la negación, son similares a los obtenidos en inglés.