Improving distant supervision using inference learning
Abstract
Distant supervision is a widely applied approach to automatic training of relation extraction systems and has the advantage that it can generate large amounts of labelled data with minimal effort. However, this data may contain errors and consequently systems trained using distant supervision tend not to perform as well as those based on manually labelled data. This work proposes a novel method for detecting potential false negative training examples using a knowledge inference method. Results show that our approach improves the performance of relation extraction systems trained using distantly supervised data.
Cite
@article{arxiv.1509.03739,
title = {Improving distant supervision using inference learning},
author = {Roland Roller and Eneko Agirre and Aitor Soroa and Mark Stevenson},
journal= {arXiv preprint arXiv:1509.03739},
year = {2015}
}
Comments
In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)