# Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention

arXiv: https://arxiv.org/abs/1804.06987 · HTML: https://mlsanity.com/paper/1804.06987/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2018.

## Built on by (most cited first, 30 of 35)

- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction](https://mlsanity.com/paper/2403.07969/) (2024)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2106.10855/) (2021)
- [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)
- [Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.09543/) (2021)
- [Mirror: A Universal Framework for Various Information Extraction Tasks](https://mlsanity.com/paper/2311.05419/) (2023)
- [MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction](https://mlsanity.com/paper/2401.09839/) (2024)
- [Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings](https://mlsanity.com/paper/2102.01156/) (2021)
- [DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2104.08655/) (2021)
- [Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs](https://mlsanity.com/paper/2105.11225/) (2021)
- [InstructIE: A Bilingual Instruction-based Information Extraction Dataset](https://mlsanity.com/paper/2305.11527/) (2023)
- [How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction](https://mlsanity.com/paper/2107.12064/) (2021)
- [HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2202.13352/) (2022)
- [Knodle: Modular Weakly Supervised Learning with PyTorch](https://mlsanity.com/paper/2104.11557/) (2021)
- [RTE: A Tool for Annotating Relation Triplets from Text](https://mlsanity.com/paper/2108.08184/) (2021)
- [Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2109.02099/) (2021)
- [IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus](https://mlsanity.com/paper/2402.14710/) (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)
- [Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering](https://mlsanity.com/paper/2108.09689/) (2021)
- [Two Training Strategies for Improving Relation Extraction over Universal Graph](https://mlsanity.com/paper/2102.06540/) (2021)
- [Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception](https://mlsanity.com/paper/2105.08213/) (2021)
- [Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2211.01432/) (2022)
- [90% F1 Score in Relational Triple Extraction: Is it Real ?](https://mlsanity.com/paper/2302.09887/) (2023)
- [Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction](https://mlsanity.com/paper/2310.18912/) (2023)
- [Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases](https://mlsanity.com/paper/2403.16396/) (2024)
- [Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis](https://mlsanity.com/paper/2502.03843/) (2025)
- [Towards Robust Universal Information Extraction: Benchmark, Evaluation, and Solution](https://mlsanity.com/paper/2503.03201/) (2025)

## Abstract (arXiv)

Relation extraction is the problem of classifying the relationship between two entities in a given sentence. Distant Supervision (DS) is a popular technique for developing relation extractors starting with limited supervision. We note that most of the sentences in the distant supervision relation extraction setting are very long and may benefit from word attention for better sentence representation. Our contributions in this paper are threefold. Firstly, we propose two novel word attention models for distantly- supervised relation extraction: (1) a Bi-directional Gated Recurrent Unit (Bi-GRU) based word attention model (BGWA), (2) an entity-centric attention model (EA), and (3) a combination model which combines multiple complementary models using weighted voting method for improved relation extraction. Secondly, we introduce GDS, a new distant supervision dataset for relation extraction. GDS removes test data noise present in all previous distant- supervision benchmark datasets, making credible automatic evaluation possible. Thirdly, through extensive experiments on multiple real-world datasets, we demonstrate the effectiveness of the proposed methods.