@InProceedings{abhishek-anand-awekar:2017:EACLlong,
  author    = {Abhishek, Abhishek  and  Anand, Ashish  and  Awekar, Amit},
  title     = {Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {797--807},
  abstract  = {Fine-grained entity type classification (FETC) is the task of classifying an
	entity mention to a broad set of types. Distant supervision paradigm is
	extensively used to generate training data for this task. However, generated
	training data assigns same set of labels to every mention of an entity without
	considering its local context. Existing FETC systems have two major drawbacks:
	assuming training data to be noise free and use of hand crafted features. Our
	work overcomes both drawbacks. We propose a neural network model that jointly
	learns entity mentions and their context representation to eliminate use of
	hand crafted features. Our model treats training data as noisy and uses
	non-parametric variant of hinge loss function. Experiments show that the
	proposed model outperforms previous state-of-the-art methods on two publicly
	available datasets, namely FIGER (GOLD) and BBN with an average relative
	improvement of 2.69\% in micro-F1 score. Knowledge learnt by our model on one
	dataset can be transferred to other datasets while using same model or other
	FETC systems. These approaches of transferring knowledge further improve the
	performance of respective models.},
  url       = {http://www.aclweb.org/anthology/E17-1075}
}