Browsing by Author "Tejada Cárcamo, Javier"

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    Clustering algorithm based on asymmetric similarity and paradigmatic features
    (Inderscience Enterprises Ltd., 2016) Santisteban Pablo, Julio Omar; Tejada Cárcamo, Javier
    Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and information retrieval. Various similarity measures are categorised in both syntactic and semantic relationships. In this paper, we present a novel similarity, unilateral Jaccard similarity coefficient (uJaccard), which does not only take into consideration the space among two points but also the semantics among them. How can we retrieve meaningful information from a large and sparse graph? Traditional approaches focus on generic clustering techniques for network graph. However, they tend to omit interesting patterns such as the paradigmatic relations. In this paper, we propose a novel graph clustering technique modelling the relations of a node using the paradigmatic analysis. Our proposed algorithm paradigmatic clustering (PaC) for graph clustering uses paradigmatic analysis supported by an asymmetric similarity using uJaccard. Extensive experiments and empirical analysis are used to evaluate our algorithm on synthetic and real data. Copyright © 2016 Inderscience Enterprises Ltd.
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    Paradigmatic Clustering for NLP
    (Institute of Electrical and Electronics Engineers Inc., 2016) Santisteban Pablo, Julio Omar; Tejada Cárcamo, Javier
    How can we retrieve meaningful information from a large and sparse graph?. Traditional approaches focus on generic clustering techniques and discovering dense cumulus in a network graph, however, they tend to omit interesting patterns such as the paradigmatic relations. In this paper, we propose a novel graph clustering technique modelling the relations of a node using the paradigmatic analysis. We exploit node's relations to extract its existing sets of signifiers. The newly found clusters represent a different view of a graph, which provides interesting insights into the structure of a sparse network graph. Our proposed algorithm PaC (Paradigmatic Clustering) for clustering graphs uses paradigmatic analysis supported by a asymmetric similarity, in contrast to traditional graph clustering methods, our algorithm yields worthy results in tasks of word-sense disambiguation. In addition we propose a novel paradigmatic similarity measure. Extensive experiments and empirical analysis are used to evaluate our algorithm on synthetic and real data. © 2015 IEEE.
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    Unilateral Weighted Jaccard Coefficient for NLP
    (Institute of Electrical and Electronics Engineers Inc., 2016) Santisteban Pablo, Julio Omar; Tejada Cárcamo, Javier
    Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various similarity measures are categorized in both syntactic and semantic relationships. In this paper we present a novel similarity, Unilateral Weighted Jaccard Coefficient (uwJaccard), which takes into consideration not only the space among two points but also the semantics among them in a distributional semantic model, the Unilateral Weighted Jaccard Coefficient provides a measure of uncertainty which will be able to measure the uncertainty among sentences such as "man bites dog" and "dog bites man". © 2015 IEEE.