# An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning

arXiv: https://arxiv.org/abs/2102.05980 · HTML: https://mlsanity.com/paper/2102.05980/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 45 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [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)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://mlsanity.com/paper/2005.06312/) (2020)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [Coreferential Reasoning Learning for Language Representation](https://mlsanity.com/paper/2004.06870/) (2020)
- [Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs](https://mlsanity.com/paper/1909.00228/) (2019)
- [Distant Supervision for Relation Extraction beyond the Sentence Boundary](https://mlsanity.com/paper/1609.04873/) (2016)
- [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018)
- [A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks](https://mlsanity.com/paper/1811.06031/) (2018)
- [Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://mlsanity.com/paper/1906.04684/) (2019)
- [Relation Classification via Recurrent Neural Network](https://mlsanity.com/paper/1508.01006/) (2015)
- [Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction](https://mlsanity.com/paper/1802.10569/) (2018)
- [Fine-tune Bert for DocRED with Two-step Process](https://mlsanity.com/paper/1909.11898/) (2019)
- [HIN: Hierarchical Inference Network for Document-Level Relation Extraction](https://mlsanity.com/paper/2003.12754/) (2020)
- [End-to-end neural relation extraction using deep biaffine attention](https://mlsanity.com/paper/1812.11275/) (2018)
- [A Survey of Deep Learning Methods for Relation Extraction](https://mlsanity.com/paper/1705.03645/) (2017)
- [Neural Relation Extraction Within and Across Sentence Boundaries](https://mlsanity.com/paper/1810.05102/) (2018)

## Built on by (most cited first, 30 of 31)

- [Revisiting Relation Extraction in the era of Large Language Models](https://mlsanity.com/paper/2305.05003/) (2023)
- [Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction](https://mlsanity.com/paper/2205.12696/) (2022)
- [A sequence-to-sequence approach for document-level relation extraction](https://mlsanity.com/paper/2204.01098/) (2022)
- [BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis](https://mlsanity.com/paper/2304.10996/) (2023)
- [Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction](https://mlsanity.com/paper/2401.13598/) (2024)
- [Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering](https://mlsanity.com/paper/2410.04752/) (2024)
- [Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs](https://mlsanity.com/paper/2305.03642/) (2023)
- [RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction](https://mlsanity.com/paper/2310.15743/) (2023)
- [Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text](https://mlsanity.com/paper/2305.03960/) (2023)
- [LLM with Relation Classifier for Document-Level Relation Extraction](https://mlsanity.com/paper/2408.13889/) (2024)
- [Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction](https://mlsanity.com/paper/2305.11029/) (2023)
- [AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify Entities](https://mlsanity.com/paper/2203.05325/) (2022)
- [A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques](https://mlsanity.com/paper/2302.08571/) (2023)
- [Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction](https://mlsanity.com/paper/2304.00824/) (2023)
- [End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies](https://mlsanity.com/paper/2304.01344/) (2023)
- [Rethinking Document-Level Relation Extraction: A Reality Check](https://mlsanity.com/paper/2306.08953/) (2023)
- [NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods](https://mlsanity.com/paper/2409.13738/) (2024)
- [Extractive Fact Decomposition for Interpretable Natural Language Inference in one Forward Pass](https://mlsanity.com/paper/2509.18901/) (2025)
- [Symlink: A New Dataset for Scientific Symbol-Description Linking](https://mlsanity.com/paper/2204.12070/) (2022)
- [Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction](https://mlsanity.com/paper/2205.01909/) (2022)
- [DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset](https://mlsanity.com/paper/2303.11141/) (2023)
- [Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models](https://mlsanity.com/paper/2310.11085/) (2023)
- [REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking](https://mlsanity.com/paper/2404.12788/) (2024)
- [A Benchmark for End-to-End Zero-Shot Biomedical Relation Extraction with LLMs: Experiments with OpenAI Models](https://mlsanity.com/paper/2504.04083/) (2025)
- [How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?](https://mlsanity.com/paper/2402.13470/) (2024)
- [On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations](https://mlsanity.com/paper/2406.07444/) (2024)
- [Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model](https://mlsanity.com/paper/2410.14225/) (2024)
- [Similarity-based Memory Enhanced Joint Entity and Relation Extraction](https://mlsanity.com/paper/2307.11762/) (2023)
- [Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER](https://mlsanity.com/paper/2403.18025/) (2024)
- [VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction](https://mlsanity.com/paper/2412.13503/) (2024)

## Abstract (arXiv)

We present a joint model for entity-level relation extraction from documents. In contrast to other approaches - which focus on local intra-sentence mention pairs and thus require annotations on mention level - our model operates on entity level. To do so, a multi-task approach is followed that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. We achieve state-of-the-art relation extraction results on the DocRED dataset and report the first entity-level end-to-end relation extraction results for future reference. Finally, our experimental results suggest that a joint approach is on par with task-specific learning, though more efficient due to shared parameters and training steps.