SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data

Abstract

We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources like lexicons as function primitives for autogenerating weak supervision. We then use a generative model to unify and denoise this supervision and construct large-scale, probabilistically labeled datasets for training high-accuracy NER taggers. In three biomedical NER tasks, SwellShark achieves competitive scores with state-of-the-art supervised benchmarks using no hand-labeled training data. In a drug name extraction task using patient medical records, one domain expert using SwellShark achieved within 5.1% of a crowdsourced annotation approach -- which originally utilized 20 teams over the course of several weeks -- in 24 hours.

References in corpus (1)

• Data Programming: Creating Large Training Sets, Quickly

Cited by in corpus (21)

• Few-shot classification in Named Entity Recognition Task
• Ontology-driven weak supervision for clinical entity classification in electronic health records
• Small Sample Learning in Big Data Era
• WRENCH: A Comprehensive Benchmark for Weak Supervision
• Terminologies augmented recurrent neural network model for clinical named entity recognition
• skweak: Weak Supervision Made Easy for NLP
• Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping
• LIREx: Augmenting Language Inference with Relevant Explanation
• Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition
• Named Entity Recognition with Partially Annotated Training Data
• Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data
• HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition
• Named Entity Recognition Only from Word Embeddings
• Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition
• Toward Open-domain Slot Filling via Self-supervised Co-training
• Weakly Supervised Named Entity Tagging with Learnable Logical Rules
• Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Models
• BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
• GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
• Building a Norwegian Lexical Resource for Medical Entity Recognition
• Open Named Entity Modeling from Embedding Distribution