Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations
Abstract
We investigate the task of distantly supervised joint entity relation extraction. It’s known that training with distant supervision will suffer from noisy samples. To tackle the problem, we propose to adapt a small manually labelled dataset to the large automatically generated dataset. By developing a novel adaptation algorithm, we are able to transfer the high quality but heterogeneous entity relation annotations in a robust and consistent way. Experiments on the benchmark NYT dataset show that our approach significantly outperforms state-ofthe-art methods.
Cite
Text
Sun and Wu. "Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33017039Markdown
[Sun and Wu. "Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/sun2019aaai-distantly/) doi:10.1609/AAAI.V33I01.33017039BibTeX
@inproceedings{sun2019aaai-distantly,
title = {{Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations}},
author = {Sun, Changzhi and Wu, Yuanbin},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {7039-7046},
doi = {10.1609/AAAI.V33I01.33017039},
url = {https://mlanthology.org/aaai/2019/sun2019aaai-distantly/}
}