Hierarchical Latent Tree Analysis for Topic Detection

  • Tengfei Liu
  • Nevin L. Zhang
  • Peixian Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8725)


In the LDA approach to topic detection, a topic is determined by identifying the words that are used with high frequency when writing about the topic. However, high frequency words in one topic may be also used with high frequency in other topics. Thus they may not be the best words to characterize the topic. In this paper, we propose a new method for topic detection, where a topic is determined by identifying words that appear with high frequency in the topic and low frequency in other topics. We model patterns of word co- occurrence and co-occurrences of those patterns using a hierarchy of discrete latent variables. The states of the latent variables represent clusters of documents and they are interpreted as topics. The words that best distinguish a cluster from other clusters are selected to characterize the topic. Empirical results show that the new method yields topics with clearer thematic characterizations than the alternative approaches.


Latent Variable Latent Dirichlet Allocation Font Size Probabilistic Graphical Model High Frequency Word 
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 2014

Authors and Affiliations

  • Tengfei Liu
    • 1
  • Nevin L. Zhang
    • 1
  • Peixian Chen
    • 1
  1. 1.Department of Computer Science and EngineeringThe Hong Kong University of Science and TechnologyHong Kong

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