Isidra Ocampo-Guzman

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In this paper an approach to construct ontologies based on a text corpus is described. Using Latent Dirichlet Allocation the topics that describe the documents contained in the corpus are identified. Each topic is formed by a set of terms whose semantic relatednesses are determined applying the distributional hypothesis, which considers as similar terms(More)
This paper proposes a methodology for the automatic learning of ontologies from a text corpus. The concepts (topics) from documents into the corpus are identified by using the Latent Dirichlet Allocation model. Based on theset of identified topics, for each concept it is constructed its taxonomy by using the terms with greater probability which contribute(More)
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