# Span Model for Open Information Extraction on Accurate Corpus

arXiv: https://arxiv.org/abs/1901.10879 · HTML: https://mlsanity.com/paper/1901.10879/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

## Built on by (most cited first, 30 of 33)

- [Text-to-Table: A New Way of Information Extraction](https://mlsanity.com/paper/2109.02707/) (2021)
- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs](https://mlsanity.com/paper/2410.02811/) (2024)
- [A Survey on Neural Open Information Extraction: Current Status and Future Directions](https://mlsanity.com/paper/2205.11725/) (2022)
- [Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset](https://mlsanity.com/paper/2211.00869/) (2022)
- [BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation](https://mlsanity.com/paper/2109.06850/) (2021)
- [DetIE: Multilingual Open Information Extraction Inspired by Object Detection](https://mlsanity.com/paper/2206.12514/) (2022)
- [DocOIE: A Document-level Context-Aware Dataset for OpenIE](https://mlsanity.com/paper/2105.04271/) (2021)
- [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://mlsanity.com/paper/2208.08690/) (2022)
- [Unified Text Structuralization with Instruction-tuned Language Models](https://mlsanity.com/paper/2303.14956/) (2023)
- [Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction](https://mlsanity.com/paper/2305.13981/) (2023)
- [Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty](https://mlsanity.com/paper/2309.03433/) (2023)
- [AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark](https://mlsanity.com/paper/2109.07464/) (2021)
- [When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications](https://mlsanity.com/paper/2211.08228/) (2022)
- [Schema-adaptable Knowledge Graph Construction](https://mlsanity.com/paper/2305.08703/) (2023)
- [milIE: Modular & Iterative Multilingual Open Information Extraction](https://mlsanity.com/paper/2110.08144/) (2021)
- [Multilingual Entity and Relation Extraction from Unified to Language-specific Training](https://mlsanity.com/paper/2301.04434/) (2023)
- [Syntactically Robust Training on Partially-Observed Data for Open Information Extraction](https://mlsanity.com/paper/2301.06841/) (2023)
- [MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction](https://mlsanity.com/paper/2308.06552/) (2023)
- [Open Information Extraction via Chunks](https://mlsanity.com/paper/2305.03299/) (2023)
- [IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models](https://mlsanity.com/paper/2210.14128/) (2022)
- [Open Set Relation Extraction via Unknown-Aware Training](https://mlsanity.com/paper/2306.04950/) (2023)
- [Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment](https://mlsanity.com/paper/2310.10590/) (2023)
- [Exploiting Duality in Open Information Extraction with Predicate Prompt](https://mlsanity.com/paper/2401.11107/) (2024)
- [Mathematical Derivation Graphs: A Relation Extraction Task in STEM Manuscripts](https://mlsanity.com/paper/2410.21324/) (2024)
- [DIAL-KG: Schema-Free Incremental Knowledge Graph Construction via Dynamic Schema Induction and Evolution-Intent Assessment](https://mlsanity.com/paper/2603.20059/) (2026)
- [Multi-Round Parsing-based Multiword Rules for Scientific OpenIE](https://mlsanity.com/paper/2108.02074/) (2021)
- [NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language](https://mlsanity.com/paper/2208.13361/) (2022)
- [Rules still work for Open Information Extraction](https://mlsanity.com/paper/2403.10758/) (2024)
- [Leveraging Linguistically Enhanced Embeddings for Open Information Extraction](https://mlsanity.com/paper/2403.13903/) (2024)

## Abstract (arXiv)

Open information extraction (Open IE) is a challenging task especially due to its brittle data basis. Most of Open IE systems have to be trained on automatically built corpus and evaluated on inaccurate test set. In this work, we first alleviate this difficulty from both sides of training and test sets. For the former, we propose an improved model design to more sufficiently exploit training dataset. For the latter, we present our accurately re-annotated benchmark test set (Re-OIE6) according to a series of linguistic observation and analysis. Then, we introduce a span model instead of previous adopted sequence labeling formulization for n-ary Open IE. Our newly introduced model achieves new state-of-the-art performance on both benchmark evaluation datasets.