@InProceedings{lin-EtAl:2018:Long3,
  author    = {Lin, Hongyu  and  Lu, Yaojie  and  Han, Xianpei  and  Sun, Le},
  title     = {Adaptive Scaling for Sparse Detection in Information Extraction},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {1033--1043},
  abstract  = {This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes. These characteristics often result in deficient performance of neural network based detection models. In this paper, we propose \emph{adaptive scaling}, an algorithm which can handle the positive sparsity problem and directly optimize over F-measure via dynamic cost-sensitive learning. To this end, we borrow the idea of marginal utility from economics and propose a theoretical framework for instance importance measuring without introducing any additional hyper-parameters. Experiments show that our algorithm leads to a more effective and stable training of neural network based detection models.},
  url       = {http://www.aclweb.org/anthology/P18-1095}
}