@InProceedings{soni-EtAl:2017:I17-2,
  author    = {Soni, Akshay  and  Pappu, Aasish  and  Ni, Jerry Chia-mau  and  Chevalier, Troy},
  title     = {Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {61--66},
  abstract  = {In Multilabel Learning (MLL) each training instance is associated with a set of
	labels and the task is to learn a function that maps an unseen instance to its
	corresponding label set. In this paper, we present a suite of -- MLL algorithm
	independent -- post-processing techniques that utilize the conditional and
	directional label-dependences in order to make the predictions from any MLL
	approach more coherent and precise. We solve  constraint optimization problem
	over the output produced by any MLL approach and the result is a refined
	version of the input predicted label set. Using proposed techniques, we show
	absolute improvement of 3% on English News and 10% on Chinese E-commerce
	datasets for P$@$K metric.},
  url       = {http://www.aclweb.org/anthology/I17-2011}
}