Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition
This work addresses data imbalance in NER for researchers and practitioners using active learning, offering an incremental improvement over existing methods.
The paper tackled the challenge of data imbalance in Named Entity Recognition (NER) for active learning by introducing a re-weighting strategy that assigns dynamic smoothed weights to tokens, resulting in substantial performance improvements on multiple corpora.
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.