@InProceedings{gridach-haddad-mulki:2017:WNUT,
  author    = {Gridach, Mourad  and  Haddad, Hatem  and  Mulki, Hala},
  title     = {Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {21--30},
  abstract  = {For brands, gaining new customer is more expensive than keeping an existing
	one. Therefore, the ability to keep customers in a brand is becoming more
	challenging these days.  Churn happens when a customer leaves a brand to
	another competitor. Most of the previous work considers the problem of churn
	prediction using the Call Detail Records (CDRs). In this paper, we use
	micro-posts to classify customers into churny or non-churny. We explore the
	power of convolutional neural networks (CNNs) since they achieved
	state-of-the-art in various computer vision and NLP applications. However, the
	robustness of end-to-end models has some limitations such as the availability
	of a large amount of labeled data and uninterpretability of these models. We
	investigate the use of CNNs augmented with structured logic rules to overcome
	or reduce this issue. We developed our system called Churn\_teacher by using an
	iterative distillation method that transfers the knowledge, extracted using
	just the combination of three logic rules, directly into the weight of the
	DNNs. Furthermore, we used weight normalization to speed up training our
	convolutional neural networks. Experimental results showed that with just these
	three rules, we were able to get state-of-the-art on publicly available Twitter
	dataset about three Telecom brands.},
  url       = {http://www.aclweb.org/anthology/W17-4403}
}