Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Chen Lin, Timothy M. Miller, Dmitriy Dligach, Hadi Amiri, Steven J. Bethard, Guergana Savova · 2018
Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance.We propose to build a recurrent neural network with multiple semantically heterogeneous embeddings within a self-training framework.Our framework makes use of labeled, unlabeled, and social media data, operates on basic features, and is scalable and generalizable.With this method, we establish the state-of-the-art result for both in-and cross-domain for a clinical temporal relation extraction task.