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End-to-end neural relation extraction using deep biaffine attention

arXiv 1812.11275 ↗First posted 29 Dec 2018cs.CL cs.IRcited by 19

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Abstract (arXiv)

We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features. The key contribution of our model is to extend a BiLSTM-CRF-based entity recognition model with a deep biaffine attention layer to model second-order interactions between latent features for relation classification, specifically attending to the role of an entity in a directional relationship. On the benchmark "relation and entity recognition" dataset CoNLL04, experimental results show that our model outperforms previous models, producing new state-of-the-art performances.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Dec 2018.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 4; 2024: 3.

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  1. 2021An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learningcited by 31
  2. 2022A sequence-to-sequence approach for document-level relation extractioncited by 25
  3. 2021Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Surveycited by 12
  4. 2024MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Constructioncited by 11
  5. 2021A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classificationcited by 8
  6. 2022AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify Entitiescited by 5
  7. 2024XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parsercited by 5
  8. 2021Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfactioncited by 2
  9. 2022Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognitioncited by 2
  10. 2022Span-based joint entity and relation extraction augmented with sequence tagging mechanismcited by 2
  11. 2023Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Eventscited by 2
  12. 2021Boosting Span-based Joint Entity and Relation Extraction via Squence Tagging Mechanismcited by 1
7 more
  1. 2022CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprisescited by 1
  2. 202390% F1 Score in Relational Triple Extraction: Is it Real ?cited by 1
  3. 2023Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Pathcited by 1
  4. 2021Deep Neural Networks for Relation Extractioncited by 0
  5. 2022PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecturecited by 0
  6. 2023Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifierscited by 0
  7. 2024AutoIE: An Automated Framework for Information Extraction from Scientific Literaturecited by 0