# A Hierarchical Framework for Relation Extraction with Reinforcement Learning

arXiv: https://arxiv.org/abs/1811.03925 · HTML: https://mlsanity.com/paper/1811.03925/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 30 of 49)

- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction](https://mlsanity.com/paper/2109.06415/) (2021)
- [A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2101.10213/) (2021)
- [ADELIE: Aligning Large Language Models on Information Extraction](https://mlsanity.com/paper/2405.05008/) (2024)
- [A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling](https://mlsanity.com/paper/2109.06705/) (2021)
- [Revisiting the Negative Data of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.10158/) (2021)
- [A Simple but Effective Bidirectional Framework for Relational Triple Extraction](https://mlsanity.com/paper/2112.04940/) (2021)
- [Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction](https://mlsanity.com/paper/2401.13598/) (2024)
- [Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors](https://mlsanity.com/paper/2404.17807/) (2024)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [Aspect Sentiment Triplet Extraction Using Reinforcement Learning](https://mlsanity.com/paper/2108.06107/) (2021)
- [Mirror: A Universal Framework for Various Information Extraction Tasks](https://mlsanity.com/paper/2311.05419/) (2023)
- [CLIP-Motion: Learning Reward Functions for Robotic Actions Using Consecutive Observations](https://mlsanity.com/paper/2311.03485/) (2023)
- [FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework](https://mlsanity.com/paper/2205.02490/) (2022)
- [JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning](https://mlsanity.com/paper/2310.02953/) (2023)
- [Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction](https://mlsanity.com/paper/2312.03022/) (2023)
- [Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification](https://mlsanity.com/paper/2403.03305/) (2024)
- [Evaluating Generative Language Models in Information Extraction as Subjective Question Correction](https://mlsanity.com/paper/2404.03532/) (2024)
- [Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks](https://mlsanity.com/paper/2505.11665/) (2025)
- [RTE: A Tool for Annotating Relation Triplets from Text](https://mlsanity.com/paper/2108.08184/) (2021)
- [IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus](https://mlsanity.com/paper/2402.14710/) (2024)
- [Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction](https://mlsanity.com/paper/2404.09593/) (2024)
- [RUIE: Retrieval-based Unified Information Extraction using Large Language Model](https://mlsanity.com/paper/2409.11673/) (2024)
- [KnowCoder-X: Boosting Multilingual Information Extraction via Code](https://mlsanity.com/paper/2411.04794/) (2024)
- [Syntactic and Semantic-driven Learning for Open Information Extraction](https://mlsanity.com/paper/2103.03448/) (2021)
- [Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering](https://mlsanity.com/paper/2108.09689/) (2021)
- [PCRED: Zero-shot Relation Triplet Extraction with Potential Candidate Relation Selection and Entity Boundary Detection](https://mlsanity.com/paper/2211.14477/) (2022)
- [CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction](https://mlsanity.com/paper/2307.00769/) (2023)

## Abstract (arXiv)

Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled. This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation. We apply a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types. The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations. Our model was evaluated on public datasets collected via distant supervision, and results show that it gains better performance than existing methods and is more powerful for extracting overlapping relations.