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Fine-grained entity typing is the task of assigning fine-grained semantictypes to entity mentions. We propose a neural architecture which learns adistributional semantic representation that leverages a greater amount ofsemantic context -- both document and sentence level information -- than priorwork. We find that additional context improves performance, with furtherimprovements gained by utilizing adaptive classification thresholds.Experiments show that our approach without reliance on hand-crafted featuresachieves the state-of-the-art results on three benchmark datasets.

Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds