@InProceedings{zhang-duh-vandurme:2018:S18-2,
  author    = {Zhang, Sheng  and  Duh, Kevin  and  Van Durme, Benjamin},
  title     = {Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds},
  booktitle = {Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {173--179},
  abstract  = {Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context -- both document and sentence level information -- than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets.},
  url       = {http://www.aclweb.org/anthology/S18-2022}
}