@InProceedings{mehri-carenini:2017:I17-1,
  author    = {Mehri, Shikib  and  Carenini, Giuseppe},
  title     = {Chat Disentanglement: Identifying Semantic Reply Relationships with Random Forests and Recurrent Neural Networks},
  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     = {615--623},
  abstract  = {Thread disentanglement is a precursor to any high-level analysis of
	multiparticipant chats. Existing research approaches the problem by calculating
	the likelihood of two messages belonging in the same thread. Our approach
	leverages a newly annotated dataset to identify reply relationships.
	Furthermore, we explore the usage of an RNN, along with large quantities of
	unlabeled data, to learn semantic relationships between messages. Our proposed
	pipeline, which utilizes a reply classifier and an RNN to generate a set of
	disentangled threads, is novel and performs well against previous work.},
  url       = {http://www.aclweb.org/anthology/I17-1062}
}