Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists. However, to be used effectively, the distant supervision must be easy to gather. In this work, we present ANEA, a tool to automatically annotate named entities in texts based on entity lists. It spans the whole pipeline from obtaining the lists to analyzing the errors of the distant supervision. A tuning step allows the user to improve the automatic annotation with their linguistic insights without labelling or checking all tokens manually. In six low-resource scenarios, we show that the F1-score can be increased by on average 18 points through distantly supervised data obtained by ANEA.
@article{arxiv.2102.13129,
title = {ANEA: Distant Supervision for Low-Resource Named Entity Recognition},
author = {Michael A. Hedderich and Lukas Lange and Dietrich Klakow},
journal= {arXiv preprint arXiv:2102.13129},
year = {2021}
}
Comments
Accepted at Practical Machine Learning For Developing Countries @ ICLR 2021