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Rich entity representations are useful for a wide class of problems involving entities.
In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and denoised distant data with standard supervised training.
Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions.
For representation, we consider representations based on the context distribution of the entity (i. e., on its embedding), on the entity's name (i. e., on its surface form) and on its description in Wikipedia.