Noise Mitigation for Neural Entity Typing and Relation Extraction

Understand

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.