Analysing Errors of Open Information Extraction Systems

Rudolf Schneider, Tom Oberhauser, Tobias Klatt, Felix A. Gers, Alexander Löser


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 on OIE systems.
Anthology ID:
W17-5402
Volume:
Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–18
Language:
URL:
https://aclanthology.org/W17-5402
DOI:
10.18653/v1/W17-5402
Bibkey:
Cite (ACL):
Rudolf Schneider, Tom Oberhauser, Tobias Klatt, Felix A. Gers, and Alexander Löser. 2017. Analysing Errors of Open Information Extraction Systems. In Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems, pages 11–18, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Analysing Errors of Open Information Extraction Systems (Schneider et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5402.pdf