@InProceedings{amoualian-EtAl:2017:Long,
  author    = {Amoualian, Hesam  and  Lu, Wei  and  Gaussier, Eric  and  Balikas, Georgios  and  Amini, Massih R  and  Clausel, Marianne},
  title     = {Topical Coherence in LDA-based Models through Induced Segmentation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1799--1809},
  abstract  = {This paper presents an LDA-based model that generates topically coherent
	segments within documents by jointly segmenting documents and assigning topics
	to their words. The coherence between topics is ensured through a copula,
	binding the topics associated to the words of a segment. In addition, this
	model relies on both document and segment specific topic distributions so as to
	capture fine grained differences in topic assignments. We show that the
	proposed model naturally encompasses other state-of-the-art LDA-based models
	designed for similar tasks. Furthermore, our experiments, conducted on six
	different publicly available datasets, show the effectiveness of our model in
	terms of perplexity, Normalized Pointwise Mutual Information, which captures
	the coherence between the generated topics, and the Micro F1 measure for text
	classification.},
  url       = {http://aclweb.org/anthology/P17-1165}
}