EZLearn: Exploiting Organic Supervision in Large-Scale Data Annotation
This addresses the bottleneck of data annotation in high-value domains like functional genomics and scientific figure comprehension, offering a scalable solution with broad applicability.
The paper tackled the problem of noisy training data in distant supervision for automated data annotation by introducing an auxiliary NLP system and co-training, achieving substantial performance improvements over state-of-the-art supervised methods without using manually labeled data.
Many real-world applications require automated data annotation, such as identifying tissue origins based on gene expressions and classifying images into semantic categories. Annotation classes are often numerous and subject to changes over time, and annotating examples has become the major bottleneck for supervised learning methods. In science and other high-value domains, large repositories of data samples are often available, together with two sources of organic supervision: a lexicon for the annotation classes, and text descriptions that accompany some data samples. Distant supervision has emerged as a promising paradigm for exploiting such indirect supervision by automatically annotating examples where the text description contains a class mention in the lexicon. However, due to linguistic variations and ambiguities, such training data is inherently noisy, which limits the accuracy of this approach. In this paper, we introduce an auxiliary natural language processing system for the text modality, and incorporate co-training to reduce noise and augment signal in distant supervision. Without using any manually labeled data, our EZLearn system learned to accurately annotate data samples in functional genomics and scientific figure comprehension, substantially outperforming state-of-the-art supervised methods trained on tens of thousands of annotated examples.