@InProceedings{alolimat-EtAl:2018:C18-11,
  author    = {Al-Olimat, Hussein  and  Gustafson, Steven  and  Mackay, Jason  and  Thirunarayan, Krishnaprasad  and  Sheth, Amit},
  title     = {A Practical Incremental Learning Framework For Sparse Entity Extraction},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {700--710},
  abstract  = {This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation. We present a framework that integrates Entity Set Expansion (ESE) and Active Learning (AL) to reduce the annotation cost of sparse data and provide an online evaluation method as feedback. This incremental and interactive learning framework allows for rapid annotation and subsequent extraction of sparse data while maintaining high accuracy.},
  url       = {http://www.aclweb.org/anthology/C18-1059}
}