Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds
Sheng Zhang, Kevin Duh, Benjamin Van Durme · 2018
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-ofthe-art results on three benchmark datasets.