# Analysing Errors of Open Information Extraction Systems

arXiv: https://arxiv.org/abs/1707.07499 · HTML: https://mlsanity.com/paper/1707.07499/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [A Survey on Neural Open Information Extraction: Current Status and Future Directions](https://mlsanity.com/paper/2205.11725/) (2022)
- [NoiseQA: Challenge Set Evaluation for User-Centric Question Answering](https://mlsanity.com/paper/2102.08345/) (2021)
- [BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation](https://mlsanity.com/paper/2109.06850/) (2021)
- [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://mlsanity.com/paper/2208.08690/) (2022)
- [AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark](https://mlsanity.com/paper/2109.07464/) (2021)
- [Cross-Register Projection for Headline Part of Speech Tagging](https://mlsanity.com/paper/2109.07483/) (2021)
- [Distantly Supervised Morpho-Syntactic Model for Relation Extraction](https://mlsanity.com/paper/2401.10002/) (2024)

## Abstract (arXiv)

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.