mlsanityData to 7 Oct 2026

Analysing Errors of Open Information Extraction Systems

arXiv 1707.07499 ↗First posted 24 Jul 2017cs.CLcited by 7

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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.

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

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Citing papers per year: 2021: 4; 2022: 2; 2023: 0; 2024: 1.

7 papers.

  1. 2022A Survey on Neural Open Information Extraction: Current Status and Future Directionscited by 27
  2. 2021NoiseQA: Challenge Set Evaluation for User-Centric Question Answeringcited by 25
  3. 2021BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluationcited by 11
  4. 2022A Survey on Open Information Extraction from Rule-based Model to Large Language Modelcited by 8
  5. 2021AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmarkcited by 7
  6. 2021Cross-Register Projection for Headline Part of Speech Taggingcited by 1
  7. 2024Distantly Supervised Morpho-Syntactic Model for Relation Extractioncited by 0