Re-weighting Tokens: A Simple and Effective Active Learning Strategy for
  Named Entity Recognition
Haocheng Luo Wei Tan Ngoc Dang Nguyen Lan Du
Active learning, a widely adopted technique for enhancing machine learning
models in text and image classification tasks with limited annotation
resources, has received relatively little attention in the domain of Named
Entity Recognition (NER). The challenge of data imbalance in NER has hindered
the effectiveness of active learning, as sequence labellers lack sufficient
learning signals. To address these challenges, this paper presents a novel
reweighting-based active learning strategy that assigns dynamic smoothed
weights to individual tokens. This adaptable strategy is compatible with
various token-level acquisition functions and contributes to the development of
robust active learners. Experimental results on multiple corpora demonstrate
the substantial performance improvement achieved by incorporating our
re-weighting strategy into existing acquisition functions, validating its
practical efficacy.