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arXiv · 1804.08000

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

Sheng Zhang·Kevin Duh·Benjamin Van Durme
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Abstract

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.

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Sheng Zhang, Kevin Duh, Benjamin Van Durme. 2018-04-21. Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds. https://arxiv.org/abs/1804.08000

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