Abstract
This paper proposes a new method for representing document collections with conceptual multidimensional spaces inferred from their contents. Such spaces are built from a set of interesting word co-occurrences, which are properly arranged into taxonomies to define orthogonal hierarchical dimensions. As a result, users can explore and analyze the contents of large document collections by making use of well-known OLAP operators (On-Line Analytic Processing) over these spaces.
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Danger, R., Berlanga, R. (2006). Inferring Multidimensional Cubes for Representing Conceptual Document Spaces. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_30
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DOI: https://doi.org/10.1007/11881216_30
Publisher Name: Springer, Berlin, Heidelberg
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