  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.
