@InProceedings{ma-cambria-gao:2016:COLING,
  author    = {Ma, Yukun  and  Cambria, Erik  and  GAO, SA},
  title     = {Label Embedding for Zero-shot Fine-grained Named Entity Typing},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {171--180},
  abstract  = {Named entity typing is the task of detecting the types of a named entity in
	context. For instance, given "Eric is giving a presentation", our goal is to
	infer that `Eric' is a speaker or a presenter and a person. Existing approaches
	to named entity typing cannot work with a growing type set and fails to
	recognize entity mentions of unseen types. In this paper, we present a label
	embedding method that incorporates prototypical and hierarchical information to
	learn pre-trained label embeddings. In addition, we adapt a zero-shot learning
	framework that can predict both seen and previously unseen entity types. We
	perform evaluation on three benchmark datasets with two settings: 1) few-shots
	recognition where all types are covered by the training set; and 2) zero-shot
	recognition where fine-grained types are assumed absent from training set.
	Results show that prior knowledge encoded using our label embedding methods can
	significantly boost the performance of classification for both cases.},
  url       = {http://aclweb.org/anthology/C16-1017}
}