@InProceedings{vishal-EtAl:2017:I17-1,
  author    = {Vishal, S  and  Yadav, Mohit  and  Vig, Lovekesh  and  Shroff, Gautam},
  title     = {Information Bottleneck Inspired Method For Chat Text Segmentation},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {194--203},
  abstract  = {We present a novel technique for segmenting chat conversations using the
	information bottleneck method (Tishby et al., 2000), augmented with sequential
	continuity constraints. Furthermore, we utilize critical non-textual clues such
	as time between two consecutive posts and people mentions within the posts. To
	ascertain the effectiveness of the proposed method, we have collected data from
	public Slack conversations and Fresco, a proprietary platform deployed inside
	our organization. Experiments demonstrate that the proposed method yields an
	absolute (relative) improvement of as high as 3.23% (11.25%). To facilitate
	future research, we are releasing manual annotations for segmentation on public
	Slack conversations.},
  url       = {http://www.aclweb.org/anthology/I17-1020}
}