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

SEMEVAL 2018  ·  Sheng Zhang, Kevin Duh, Benjamin Van Durme ·

Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context -- both document and sentence level information -- than prior work... We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets. read more

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