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.
@article{arxiv.1804.08000,
title = {Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds},
author = {Sheng Zhang and Kevin Duh and Benjamin Van Durme},
journal= {arXiv preprint arXiv:1804.08000},
year = {2018}
}