@inproceedings{2d525a2945b344599df79a7bd6d10f90,
title = "Filling knowledge base gaps for distant supervision of relation extraction",
abstract = "Distant supervision has attracted recent interest for training information extraction systems because it does not require any human annotation but rather isting knowledge bases to he bel a training corpus. Howe work has failed to address of false negative training exa beled due to the incompleten edge bases. To tackle this propose a simple yet novel fr combines a passage retrieval coarse features into a state-o tion extractor using multi-in ing with fine features. We formation retrieval techniqu relevance feedback to expan bases, assuming entity pairs i passages are more likely to e tion. Our proposed technique improves the quality of dis vised relation extraction, bo from 47.7% to 61.2% with a high level of precision of around 93% in the experiments.",
author = "Wei Xu and Raphael Hoffmann and Le Zhao and Ralph Grishman",
year = "2013",
language = "English (US)",
isbn = "9781937284510",
series = "ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "665--670",
booktitle = "Short Papers",
note = "51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 ; Conference date: 04-08-2013 Through 09-08-2013",
}