Active Learning from Crowd in Document Screening

TL;DR

This paper presents a novel active learning approach that efficiently combines crowdsourcing and machine learning for document screening, optimizing the use of limited budgets to improve classification accuracy.

Contribution

It introduces objective-aware sampling, a new active learning technique that prioritizes minimizing overall classification errors in multi-label document screening tasks.

Findings

Objective-aware sampling outperforms existing active learning strategies.

The method effectively allocates labeling resources to improve classifier performance.

Significant reduction in classification errors achieved with limited budgets.

Abstract

In this paper, we explore how to efficiently combine crowdsourcing and machine intelligence for the problem of document screening, where we need to screen documents with a set of machine-learning filters. Specifically, we focus on building a set of machine learning classifiers that evaluate documents, and then screen them efficiently. It is a challenging task since the budget is limited and there are countless number of ways to spend the given budget on the problem. We propose a multi-label active learning screening specific sampling technique -- objective-aware sampling -- for querying unlabelled documents for annotating. Our algorithm takes a decision on which machine filter need more training data and how to choose unlabeled items to annotate in order to minimize the risk of overall classification errors rather than minimizing a single filter error. We demonstrate that objective-aware sampling significantly outperforms the state of the art active learning sampling strategies.