We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems. Our comprehensive benchmark contains three data sets from the news domain and one data set from Wikipedia with overall 4522 labeled sentences and 11243 binary or n-ary OIE relations. In our analysis on these data sets we compared the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford OpenIE and PredPatt. In addition, we evaluated the impact of five common error classes on a subset of 749 n-ary tuples. From our deep analysis we unreveal important research directions for a next generation of OIE systems.
Cite
@article{arxiv.1707.07499,
title = {Analysing Errors of Open Information Extraction Systems},
author = {Rudolf Schneider and Tom Oberhauser and Tobias Klatt and Felix A. Gers and Alexander Löser},
journal= {arXiv preprint arXiv:1707.07499},
year = {2017}
}
Comments
Accepted at Building Linguistically Generalizable NLP Systems at EMNLP 2017