Probabilistic Explicit Topic Modeling Using Wikipedia

  • Joshua A. Hansen
  • Eric K. Ringger
  • Kevin D. Seppi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8105)


Despite popular use of Latent Dirichlet Allocation (LDA) for automatic discovery of latent topics in document corpora, such topics lack connections with relevant knowledge sources such as Wikipedia, and they can be difficult to interpret due to the lack of meaningful topic labels. Furthermore, the topic analysis suffers from a lack of identifiability between topics across independently analyzed corpora but also across distinct runs of the algorithm on the same corpus. This paper introduces two methods for probabilistic explicit topic modeling that address these issues: Latent Dirichlet Allocation with Static Topic-Word Distributions (LDA-STWD), and Explicit Dirichlet Allocation (EDA). Both of these methods estimate topic-word distributions a priori from Wikipedia articles, with each article corresponding to one topic and the article title serving as a topic label. LDA-STWD and EDA overcome the nonidentifiability, isolation, and unintepretability of LDA output. We assess their effectiveness by means of crowd-sourced user studies on two tasks: topic label generation and document label generation. We find that LDA-STWD improves substantially upon the performance of the state-of-the-art on the document labeling task, and that both methods otherwise perform on par with a state-of-the-art post hoc method.


Latent Dirichlet Allocation Latent Semantic Analysis Probabilistic Latent Semantic Analysis Label Quality Label Pair 
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 2013

Authors and Affiliations

  • Joshua A. Hansen
    • 1
  • Eric K. Ringger
    • 1
  • Kevin D. Seppi
    • 1
  1. 1.Department of Computer ScienceBrigham Young UniversityProvoUSA

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