@InProceedings{shimaoka-EtAl:2017:EACLlong,
  author    = {Shimaoka, Sonse  and  Stenetorp, Pontus  and  Inui, Kentaro  and  Riedel, Sebastian},
  title     = {Neural Architectures for Fine-grained Entity Type Classification},
  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     = {1271--1280},
  abstract  = {In this work, we investigate several neural network architectures for
	fine-grained entity type classification and make three key contributions. 
	Despite being a natural comparison and addition, previous work on attentive
	neural architectures have not considered hand-crafted features and we combine
	these with learnt features and establish that they complement each other. 
	Additionally, through quantitative analysis we establish that the attention
	mechanism learns to attend over syntactic heads and the phrase containing the
	mention, both of which are known to be strong hand-crafted features for our
	task.  We introduce parameter sharing between labels through a hierarchical
	encoding method, that in low-dimensional projections show clear clusters for
	each type hierarchy.  Lastly, despite using the same evaluation dataset, the
	literature frequently compare models trained using different data.  We
	demonstrate that the choice of training data has a drastic impact on
	performance, which decreases by as much as 9.85% loose micro F1 score for a
	previously proposed method.  Despite this discrepancy, our best model achieves
	state-of-the-art results with 75.36% loose micro F1 score on the
	well-established Figer (GOLD) dataset and we report the best results for models
	trained using publicly available data for the OntoNotes dataset with 64.93%
	loose micro F1 score.},
  url       = {http://www.aclweb.org/anthology/E17-1119}
}