# Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions

arXiv: https://arxiv.org/abs/1904.00143 · HTML: https://mlsanity.com/paper/1904.00143/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2019.

## Built on by (most cited first, 27 of 27)

- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction](https://mlsanity.com/paper/2106.00459/) (2021)
- [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)
- [SENT: Sentence-level Distant Relation Extraction via Negative Training](https://mlsanity.com/paper/2106.11566/) (2021)
- [Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs](https://mlsanity.com/paper/2105.11225/) (2021)
- [Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors](https://mlsanity.com/paper/2104.08225/) (2021)
- [Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training](https://mlsanity.com/paper/2106.10835/) (2021)
- [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)
- [Linguistic representations for fewer-shot relation extraction across domains](https://mlsanity.com/paper/2307.03823/) (2023)
- [ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction](https://mlsanity.com/paper/2309.01370/) (2023)
- [Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering](https://mlsanity.com/paper/2108.09689/) (2021)
- [Document-level Relation Extraction with Relation Correlations](https://mlsanity.com/paper/2212.10171/) (2022)
- [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)
- [Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction](https://mlsanity.com/paper/2310.18912/) (2023)
- [A systematic review of relation extraction task since the emergence of Transformers](https://mlsanity.com/paper/2511.03610/) (2025)
- [Deep Neural Networks for Relation Extraction](https://mlsanity.com/paper/2104.01799/) (2021)
- [A Sample-Based Training Method for Distantly Supervised Relation Extraction with Pre-Trained Transformers](https://mlsanity.com/paper/2104.07512/) (2021)
- [A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents](https://mlsanity.com/paper/2108.09505/) (2021)
- [Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision](https://mlsanity.com/paper/2109.09036/) (2021)
- [Few Clean Instances Help Denoising Distant Supervision](https://mlsanity.com/paper/2209.06596/) (2022)
- [FGSI: Distant Supervision for Relation Extraction method based on Fine-Grained Semantic Information](https://mlsanity.com/paper/2302.02078/) (2023)
- [Document-Level Relation Extraction with Relation Correlation Enhancement](https://mlsanity.com/paper/2310.13000/) (2023)

## Abstract (arXiv)

This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-noising by designing neural networks with intra-bag attentions. In this paper, both intra-bag and inter-bag attentions are considered in order to deal with the noise at sentence-level and bag-level respectively. First, relation-aware bag representations are calculated by weighting sentence embeddings using intra-bag attentions. Here, each possible relation is utilized as the query for attention calculation instead of only using the target relation in conventional methods. Furthermore, the representation of a group of bags in the training set which share the same relation label is calculated by weighting bag representations using a similarity-based inter-bag attention module. Finally, a bag group is utilized as a training sample when building our relation extractor. Experimental results on the New York Times dataset demonstrate the effectiveness of our proposed intra-bag and inter-bag attention modules. Our method also achieves better relation extraction accuracy than state-of-the-art methods on this dataset.