1 research outputs found
Adaptive Scaling for Sparse Detection in Information Extraction
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 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.Comment: Accepted to ACL201