Admixture of Poisson MRFs : A Topic Model with Word Dependencies

@inproceedings{DINOUYE2014AdmixtureOP,
  title={Admixture of Poisson MRFs : A Topic Model with Word Dependencies},
  author={DINOUYE and CS. UTEXAS and PRADEEPR and CS. UTEXAS. EDU and Inderjit},
  year={2014}
}
  • DINOUYE, CS. UTEXAS, +2 authors Inderjit
  • Published 2014
David I. Inouye DINOUYE@CS.UTEXAS.EDU Pradeep Ravikumar PRADEEPR@CS.UTEXAS.EDU Inderjit S. Dhillon INDERJIT@CS.UTEXAS.EDU Dept. of Computer Science, University of Texas, Austin, TX 78712, USA Abstract This paper introduces a new topic model based on an admixture of Poisson Markov Random Fields (APM), which can model dependencies between words as opposed to previous independent topic models such as PLSA (Hofmann, 1999), LDA (Blei et al., 2003) or SAM (Reisinger et al., 2010). We propose a class… CONTINUE READING
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