Topic Significance Ranking of LDA Generative Models

@inproceedings{AlSumait2009TopicSR,
  title={Topic Significance Ranking of LDA Generative Models},
  author={Loulwah AlSumait and Daniel Barbar{\'a} and James Gentle and Carlotta Domeniconi},
  booktitle={ECML/PKDD},
  year={2009}
}
Topic models, like Latent Dirichlet Allocation (LDA), have been recently used to automatically generate text corpora topics, and to subdivide the corpus words among those topics. However, not all the estimated topics are of equal importance or correspond to genuine themes of the domain. Some of the topics can be a collection of irrelevant or background words, or represent insignificant themes. Current approaches to topic modeling perform manual examination of their output to find meaningful and… CONTINUE READING
BETA

Citations

Publications citing this paper.
SHOWING 1-10 OF 74 CITATIONS, ESTIMATED 27% COVERAGE

FILTER CITATIONS BY YEAR

2010
2019

CITATION STATISTICS

  • 9 Highly Influenced Citations

  • Averaged 10 Citations per year over the last 3 years

Similar Papers

Loading similar papers…