# InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction

arXiv: https://arxiv.org/abs/2012.09370 · HTML: https://mlsanity.com/paper/2012.09370/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction](https://mlsanity.com/paper/2404.09593/) (2024)
- [Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision](https://mlsanity.com/paper/2109.09036/) (2021)
- [A Benchmark for Text Expansion: Datasets, Metrics, and Baselines](https://mlsanity.com/paper/2309.09198/) (2023)
- [Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances](https://mlsanity.com/paper/2310.16790/) (2023)

## Abstract (arXiv)

Distant supervision makes it possible to automatically label bags of sentences for relation extraction by leveraging knowledge bases, but suffers from the sparse and noisy bag issues. Additional information sources are urgently needed to supplement the training data and overcome these issues. In this paper, we introduce two widely-existing sources in knowledge bases, namely entity descriptions, and multi-grained entity types to enrich the distantly supervised data. We see information sources as multiple views and fusing them to construct an intact space with sufficient information. An end-to-end multi-view learning framework is proposed for relation extraction via Intact Space Representation Learning (InSRL), and the representations of single views are jointly learned simultaneously. Moreover, inner-view and cross-view attention mechanisms are used to highlight important information on different levels on an entity-pair basis. The experimental results on a popular benchmark dataset demonstrate the necessity of additional information sources and the effectiveness of our framework. We will release the implementation of our model and dataset with multiple information sources after the anonymized review phase.