Self-training improves recurrent neural networks performance for temporal relation extraction
Published in Proceedings of the ninth international workshop on health text mining and …, 2018, 2018
Recommended citation: Chen Lin, Timothy A Miller, Dmitriy Dligach, Hadi Amiri, Steven Bethard, and Guergana K Savova. 2018. Self-training improves recurrent neural networks performance for temporal relation extraction. In Proceedings of the ninth international workshop on health text mining and …, 2018. https://aclanthology.org/W18-5619.pdf
Abstract:
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.