Few-shot Learning for Named Entity Recognition in Medical Text

Understand

Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017).

• However, these gains rely on the availability of large amounts of annotated examples, without which state-of-the-art performance is rarely achievable.
• This is especially inconvenient for the many NLP fields where annotated examples are scarce, such as medical text.
• To improve NLP models in this situation, we evaluate five improvements on named entity recognition (NER) tasks when only ten annotated examples are available: (1) layer-wise initialization with pre-trained weights, (2) hyperparameter tuning, (3) combining pre-training data, (4) custom word embeddings, and (5) optimizing out-of-vocabulary (OOV) words.