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|Title:||Paradigmatic Clustering for NLP|
|Authors:||Santisteban Pablo, Julio Omar|
Tejada Cárcamo, Javier
|Keywords:||Cluster analysis;Data mining;Graph theory;Natural language processing systems;asymmetric similarity;clustering;Clustering techniques;paradigmatic;Similarity measure;Synthetic and real data;Traditional approaches;Word Sense Disambiguation;Clustering algorithms|
|Publisher:||Institute of Electrical and Electronics Engineers Inc.|
|Abstract:||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.|
|Appears in Collections:||Artículos de investigación|
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