@InProceedings{zhao-masino-yang:2018:BioNLP18,
  author    = {Zhao, Mengnan  and  Masino, Aaron J.  and  Yang, Christopher C.},
  title     = {A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity},
  booktitle = {Proceedings of the BioNLP 2018 workshop},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {156--160},
  abstract  = {We investigate the quality of task specific word embeddings created with relatively small, targeted corpora. We present a comprehensive evaluation framework including both intrinsic and extrinsic evaluation that can be expanded to named entities beyond drug name. Intrinsic evaluation results tell that drug name embeddings created with a domain specific document corpus outperformed the previously published versions that derived from a very large general text corpus. Extrinsic evaluation uses word embedding for the task of drug name recognition with Bi-LSTM model and the results demonstrate the advantage of using domain-specific word embeddings as the only input feature for drug name recognition with F1-score achieving 0.91. This work suggests that it may be advantageous to derive domain specific embeddings for certain tasks even when the domain specific corpus is of limited size.},
  url       = {http://www.aclweb.org/anthology/W18-2319}
}