Noise Mitigation for Neural Entity Typing and Relation Extraction
This addresses noise issues in information extraction for NLP applications, but it is incremental as it builds on existing methods.
The paper tackled noise from distant supervision and pipeline input features in entity typing and relation extraction, achieving comparable performance to state-of-the-art supervised methods and showing that probabilistic predictions and joint training improve robustness.
In this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features. Our target tasks are entity typing and relation extraction. For the first noise type, we introduce multi-instance multi-label learning algorithms using neural network models, and apply them to fine-grained entity typing for the first time. This gives our models comparable performance with the state-of-the-art supervised approach which uses global embeddings of entities. For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction. Our experiments show that probabilistic predictions are more robust than discrete predictions and that joint training of the two tasks performs best.