mlsanityData to 7 Oct 2026

DocOIE: A Document-level Context-Aware Dataset for OpenIE

arXiv 2105.04271 ↗First posted 10 May 2021cs.CLcited by 10

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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

  1. 2017Attention Is All You Needcited by 59,932
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  7. 2019Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputscited by 38
  8. 2020IMoJIE: Iterative Memory-Based Joint Open Information Extractioncited by 34
  9. 2019Span Model for Open Information Extraction on Accurate Corpuscited by 33
  10. 2017Answering Complex Questions Using Open Information Extractioncited by 25
  11. 2019OPIEC: An Open Information Extraction Corpuscited by 20
  12. 2018WiRe57 : A Fine-Grained Benchmark for Open Information Extractioncited by 16
1 more
  1. 2018Graphene: Semantically-Linked Propositions in Open Information Extractioncited by 8

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 1; 2025: 2.

10 papers.