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Analysing Errors of Open Information Extraction Systems
July 24, 2017 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Rudolf Schneider, Tom Oberhauser, Tobias Klatt, Felix A. Gers, Alexander Lรถser
arXiv ID
1707.07499
Category
cs.CL: Computation & Language
Citations
35
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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