Active learning is well-suited to many problems in natural language processing, where unlabeled data may be abundant but annotation is slow and expensive. This paper aims to shed light on the best active learning approaches for sequence labeling tasks such as information extraction and document segmentation. We survey previously used query selection strategies for sequence models, and propose several novel algorithms to address their shortcomings. We also conduct a large-scale empirical comparison using multiple corpora, which demonstrates that our proposed methods advance the state of the art.
REF propose approaches to perform active learning for sequence labeling tasks, including uncertainty sampling and QBC.
An Analysis of Active Learning Strategies for Sequence Labeling Tasks
Conference On Empirical Methods In Natural Language Processing
Computer Science