@InProceedings{chen-EtAl:2017:RepL4NLP,
  author    = {Chen, Sheng  and  Soni, Akshay  and  Pappu, Aasish  and  Mehdad, Yashar},
  title     = {DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {111--120},
  abstract  = {Tagging news articles or blog posts with relevant tags from a collection of
	predefined ones is coined as document tagging in this work. Accurate tagging of
	articles can benefit several downstream applications such as recommendation and
	search. In this work, we propose a novel yet simple approach called DocTag2Vec
	to accomplish this task. We substantially extend Word2Vec and Doc2Vec -- two
	popular models for learning  distributed representation of words and documents.
	In DocTag2Vec, we simultaneously learn the representation of words, documents,
	and tags in a joint vector space during training, and employ the simple
	k-nearest neighbor search to predict tags for unseen documents. In contrast to
	previous multi-label learning methods, DocTag2Vec directly deals with raw text
	instead of provided feature vector, and in addition, enjoys advantages like the
	learning of tag representation, and the ability of handling newly created tags.
	To demonstrate the effectiveness of our approach, we conduct experiments on
	several datasets and show promising results against state-of-the-art methods.},
  url       = {http://www.aclweb.org/anthology/W17-2614}
}