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

arXiv: https://arxiv.org/abs/1812.11275 · HTML: https://mlsanity.com/paper/1812.11275/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2018.

## Built on by (most cited first, 19 of 19)

- [An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning](https://mlsanity.com/paper/2102.05980/) (2021)
- [A sequence-to-sequence approach for document-level relation extraction](https://mlsanity.com/paper/2204.01098/) (2022)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction](https://mlsanity.com/paper/2401.09839/) (2024)
- [A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification](https://mlsanity.com/paper/2110.09570/) (2021)
- [AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify Entities](https://mlsanity.com/paper/2203.05325/) (2022)
- [XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser](https://mlsanity.com/paper/2405.17336/) (2024)
- [Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction](https://mlsanity.com/paper/2103.11062/) (2021)
- [Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition](https://mlsanity.com/paper/2207.03300/) (2022)
- [Span-based joint entity and relation extraction augmented with sequence tagging mechanism](https://mlsanity.com/paper/2210.12720/) (2022)
- [Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events](https://mlsanity.com/paper/2302.04185/) (2023)
- [Boosting Span-based Joint Entity and Relation Extraction via Squence Tagging Mechanism](https://mlsanity.com/paper/2105.10080/) (2021)
- [CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprises](https://mlsanity.com/paper/2210.10581/) (2022)
- [90% F1 Score in Relational Triple Extraction: Is it Real ?](https://mlsanity.com/paper/2302.09887/) (2023)
- [Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path](https://mlsanity.com/paper/2305.13805/) (2023)
- [Deep Neural Networks for Relation Extraction](https://mlsanity.com/paper/2104.01799/) (2021)
- [PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture](https://mlsanity.com/paper/2211.12157/) (2022)
- [Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers](https://mlsanity.com/paper/2304.14447/) (2023)
- [AutoIE: An Automated Framework for Information Extraction from Scientific Literature](https://mlsanity.com/paper/2401.16672/) (2024)

## 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.