Topics in a large collection of texts

Xem 1-3 trên 3 kết quả Topics in a large collection of texts
  • We describe an original method that automatically finds specific topics in a large collection of texts. Each topic is first identified as a specific cluster of texts and then represented as a virtual concept, which is a weighted mixture of words. Our intention is to employ these virtual concepts in document indexing. In this paper we show some preliminary experimental results and discuss directions of future work.

    pdf6p bunbo_1 17-04-2013 44 2   Download

  • Probabilistic topic models have recently gained much popularity in informational retrieval and related areas. Via such models, one can project high-dimensional objects such as text documents into a low dimensional space where their latent semantics are captured and modeled; can integrate multiple sources of information—to ”share statistical strength” among components of a hierarchical probabilistic model; and can structurally display and classify the otherwise unstructured object collections. ...

    pdf1p nghetay_1 07-04-2013 30 1   Download

  • The problem of posterior inference for individual documents is particularly important in topic models. However, it is often intractable in practice. Many existing methods for posterior inference such as variational Bayes, collapsed variational Bayes and collapsed Gibbs sampling do not have any guarantee on either quality or rate of convergence.

    pdf13p vimariecurie2711 30-07-2019 7 0   Download



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