# Open Information Extraction via Chunks

arXiv: https://arxiv.org/abs/2305.03299 · HTML: https://mlsanity.com/paper/2305.03299/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [Neural Open Information Extraction](https://mlsanity.com/paper/1805.04270/) (2018)
- [IMoJIE: Iterative Memory-Based Joint Open Information Extraction](https://mlsanity.com/paper/2005.08178/) (2020)
- [Span Model for Open Information Extraction on Accurate Corpus](https://mlsanity.com/paper/1901.10879/) (2019)
- [A Survey on Neural Open Information Extraction: Current Status and Future Directions](https://mlsanity.com/paper/2205.11725/) (2022)
- [Answering Complex Questions Using Open Information Extraction](https://mlsanity.com/paper/1704.05572/) (2017)
- [Robust Multilingual Part-of-Speech Tagging via Adversarial Training](https://mlsanity.com/paper/1711.04903/) (2017)
- [LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction](https://mlsanity.com/paper/2101.11177/) (2021)
- [BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation](https://mlsanity.com/paper/2109.06850/) (2021)
- [DocOIE: A Document-level Context-Aware Dataset for OpenIE](https://mlsanity.com/paper/2105.04271/) (2021)
- [milIE: Modular & Iterative Multilingual Open Information Extraction](https://mlsanity.com/paper/2110.08144/) (2021)
- [Syntactic Multi-view Learning for Open Information Extraction](https://mlsanity.com/paper/2212.02068/) (2022)
- [Syntactically Robust Training on Partially-Observed Data for Open Information Extraction](https://mlsanity.com/paper/2301.06841/) (2023)

## Built on by (most cited first, 2 of 2)

- [Evaluation of Table Representations to Answer Questions from Tables in Documents : A Case Study using 3GPP Specifications](https://mlsanity.com/paper/2408.17008/) (2024)
- [Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model](https://mlsanity.com/paper/2606.17950/) (2026)

## Abstract (arXiv)

Open Information Extraction (OIE) aims to extract relational tuples from open-domain sentences. Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments. We instead propose Sentence as Chunk sequence (SaC) and recognize chunk spans as tuple relations and arguments. We argue that SaC has better quantitative and qualitative properties for OIE than sentence as token sequence, and evaluate four choices of chunks (i.e., CoNLL chunks, simple phrases, NP chunks, and spans from SpanOIE) against gold OIE tuples. Accordingly, we propose a simple BERT-based model for sentence chunking, and propose Chunk-OIE for tuple extraction on top of SaC. Chunk-OIE achieves state-of-the-art results on multiple OIE datasets, showing that SaC benefits OIE task.