# Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks arXiv: https://arxiv.org/abs/2310.17238 ยท HTML: https://mlsanity.com/paper/2310.17238/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 34 of its 54 references that are in the map. - First posted on arXiv 26 Oct 2023. ## Builds on (most cited first, 30 of 34) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019) - [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019) - [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018) - [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016) - [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018) - [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019) - [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019) - [A Note on Over-Smoothing for Graph Neural Networks](https://mlsanity.com/paper/2006.13318/) (2020) - [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019) - [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022) - [Conditional Random Fields as Recurrent Neural Networks](https://mlsanity.com/paper/1502.03240/) (2015) - [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021) - [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016) - [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019) - [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://mlsanity.com/paper/2105.00956/) (2021) - [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020) - [Neural Enhanced Belief Propagation on Factor Graphs](https://mlsanity.com/paper/2003.01998/) (2020) - [HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs](https://mlsanity.com/paper/2010.04558/) (2020) - [Efficient Second-Order TreeCRF for Neural Dependency Parsing](https://mlsanity.com/paper/2005.00975/) (2020) - [A Partition Filter Network for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2108.12202/) (2021) - [Inference in Probabilistic Graphical Models by Graph Neural Networks](https://mlsanity.com/paper/1803.07710/) (2018) - [UniRE: A Unified Label Space for Entity Relation Extraction](https://mlsanity.com/paper/2107.04292/) (2021) - [Belief Propagation Neural Networks](https://mlsanity.com/paper/2007.00295/) (2020) - [A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2101.10213/) (2021) - [Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks](https://mlsanity.com/paper/2103.09330/) (2021) - [Second-Order Semantic Dependency Parsing with End-to-End Neural Networks](https://mlsanity.com/paper/1906.07880/) (2019) - [Denoising Relation Extraction from Document-level Distant Supervision](https://mlsanity.com/paper/2011.03888/) (2020) - [Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training](https://mlsanity.com/paper/2010.05003/) (2020) - [Factor Graph Neural Networks](https://mlsanity.com/paper/2308.00887/) (2023) ## Built on by (most cited first, 11 of 11) - [SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents](https://mlsanity.com/paper/2410.21155/) (2024) - [SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP](https://mlsanity.com/paper/2509.07801/) (2025) - [PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction](https://mlsanity.com/paper/2405.20787/) (2024) - [Beyond path selection: Better LLMs for Scientific Information Extraction with MimicSFT and Relevance and Rule-induced(R2)GRPO](https://mlsanity.com/paper/2505.22068/) (2025) - [Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies](https://mlsanity.com/paper/2604.20548/) (2026) - [LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction](https://mlsanity.com/paper/2607.23420/) (2026) - [Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement](https://mlsanity.com/paper/2501.09451/) (2025) - [BioPIE: A Biomedical Protocol Information Extraction Dataset for Experiment Understanding](https://mlsanity.com/paper/2601.04524/) (2026) - [Knowledge Augmented Entity and Relation Extraction for Legal Documents with Hypergraph Neural Network](https://mlsanity.com/paper/2602.08289/) (2026) - [HGNet: Scalable Foundation Model for Automated Knowledge Graph Generation from Scientific Literature](https://mlsanity.com/paper/2603.23136/) (2026) - [Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey](https://mlsanity.com/paper/2607.09666/) (2026) ## Abstract (arXiv) Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial.In this work, we propose HyperGraph neural network for ERE (hgnn), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model). To alleviate error propagation,we use a high-recall pruner mechanism to transfer the burden of entity identification and labeling from the NER module to the joint module of our model. For higher-order modeling, we build a hypergraph, where nodes are entities (provided by the span pruner) and relations thereof, and hyperedges encode interactions between two different relations or between a relation and its associated subject and object entities. We then run a hypergraph neural network for higher-order inference by applying message passing over the built hypergraph. Experiments on three widely used benchmarks (acef, ace and scierc) for ERE task show significant improvements over the previous state-of-the-art PL-marker.