Adaptive Scaling for Sparse Detection in Information Extraction

Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun


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 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.
Anthology ID:
P18-1095
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1033–1043
Language:
URL:
https://aclanthology.org/P18-1095
DOI:
10.18653/v1/P18-1095
Bibkey:
Cite (ACL):
Hongyu Lin, Yaojie Lu, Xianpei Han, and Le Sun. 2018. Adaptive Scaling for Sparse Detection in Information Extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1033–1043, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Adaptive Scaling for Sparse Detection in Information Extraction (Lin et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1095.pdf
Video:
 https://vimeo.com/285802176