Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition

TL;DR

This paper introduces a re-weighting-based active learning strategy for Named Entity Recognition that dynamically adjusts token importance, significantly improving model performance on various datasets.

Contribution

It proposes a novel re-weighting method for active learning in NER, enhancing effectiveness across different acquisition functions.

Findings

Substantial performance improvements on multiple NER corpora

Re-weighting strategy is compatible with various acquisition functions

Enhances robustness of active learners in NER tasks

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.