DocOIE: A Document-level Context-Aware Dataset for OpenIE
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Abstract (arXiv)
Open Information Extraction (OpenIE) aims to extract structured relational tuples (subject, relation, object) from sentences and plays critical roles for many downstream NLP applications. Existing solutions perform extraction at sentence level, without referring to any additional contextual information. In reality, however, a sentence typically exists as part of a document rather than standalone; we often need to access relevant contextual information around the sentence before we can accurately interpret it. As there is no document-level context-aware OpenIE dataset available, we manually annotate 800 sentences from 80 documents in two domains (Healthcare and Transportation) to form a DocOIE dataset for evaluation. In addition, we propose DocIE, a novel document-level context-aware OpenIE model. Our experimental results based on DocIE demonstrate that incorporating document-level context is helpful in improving OpenIE performance. Both DocOIE dataset and DocIE model are released for public.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 10 May 2021.
Builds on
13 of 30 references.
References · 13
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2016Incorporating Copying Mechanism in Sequence-to-Sequence Learningcited by 217
- 2018A Survey on Open Information Extractioncited by 51
- 2018Neural Open Information Extractioncited by 50
- 2019Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputscited by 38
- 2020IMoJIE: Iterative Memory-Based Joint Open Information Extractioncited by 34
- 2019Span Model for Open Information Extraction on Accurate Corpuscited by 33
- 2017Answering Complex Questions Using Open Information Extractioncited by 25
- 2019OPIEC: An Open Information Extraction Corpuscited by 20
- 2018WiRe57 : A Fine-Grained Benchmark for Open Information Extractioncited by 16
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Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 1; 2025: 2.
10 papers.
- 2022BioRED: A Rich Biomedical Relation Extraction Datasetcited by 84
- 2025MMDocIR: Benchmarking Multimodal Retrieval for Long Documentscited by 43
- 2022A Survey on Neural Open Information Extraction: Current Status and Future Directionscited by 27
- 2024Automated Construction of Theme-specific Knowledge Graphscited by 15
- 2021BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluationcited by 11
- 2023Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extractioncited by 8
- 2022Syntactic Multi-view Learning for Open Information Extractioncited by 3
- 2023Open Information Extraction via Chunkscited by 2
- 2025Zero-Shot Open-Schema Entity Structure Discoverycited by 1
- 2023Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detectioncited by 0
- 2025Zero-Shot Open-Schema Entity Structure Discovery4 Jun 2025
- 2025MMDocIR: Benchmarking Multimodal Retrieval for Long Documents15 Jan 2025
- 2024Automated Construction of Theme-specific Knowledge Graphs29 Apr 2024
- 2023Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction23 May 2023
- 2023Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection7 May 2023
- 2023Open Information Extraction via Chunks5 May 2023
- 2022Syntactic Multi-view Learning for Open Information Extraction5 Dec 2022
- 2022A Survey on Neural Open Information Extraction: Current Status and Future Directions24 May 2022
- 2022BioRED: A Rich Biomedical Relation Extraction Dataset8 Apr 2022
- 2021BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation14 Sep 2021