# Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition Source: [Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:57:41.618Z Authors: Haocheng Luo, Wei Tan, Ngoc Dang Nguyen, Lan Du Published: 2023-11-02T00:19:02.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()