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Clustering Textual Data by Latent Dirichlet Allocation: Applications and Extensions to Hierarchical Data

  • Matteo DimaiEmail author
  • Nicola Torelli
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Latent Dirichlet Allocation is a generative probabilistic model that can be used to describe and analyse textual data. We extend the basic LDA model to search and classify a large set of administrative documents taking into account the structure of the textual data that show a clear hierarchy. This can be considered as a general approach to the analysis of short texts semantically linked to larger texts. Some preliminary empirical evidence that support the proposed model is presented.

Keywords

Latent Dirichlet Allocation Latent Semantic Analysis Probabilistic Latent Semantic Analysis Latent Dirichlet Allocation Model Probabilistic Generative Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Dept. of Economics and StatisticsUniversity of TriesteTriesteItaly

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