# Noise Mitigation for Neural Entity Typing and Relation Extraction

arXiv: https://arxiv.org/abs/1612.07495 · HTML: https://mlsanity.com/paper/1612.07495/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2016.

## Built on by (most cited first, 15 of 15)

- [Fine-grained Entity Typing via Label Reasoning](https://mlsanity.com/paper/2109.05744/) (2021)
- [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)
- [Improving Scene Graph Classification by Exploiting Knowledge from Texts](https://mlsanity.com/paper/2102.04760/) (2021)
- [Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation](https://mlsanity.com/paper/2206.13746/) (2022)
- [Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2109.02099/) (2021)
- [All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing](https://mlsanity.com/paper/2410.17355/) (2024)
- [Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation](https://mlsanity.com/paper/2501.14166/) (2025)
- [Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering](https://mlsanity.com/paper/2108.09689/) (2021)
- [AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations](https://mlsanity.com/paper/2308.16055/) (2023)
- [Beyond the Numbers: Transparency in Relation Extraction Benchmark Creation and Leaderboards](https://mlsanity.com/paper/2411.05224/) (2024)
- [Relation Extraction or Pattern Matching? Unravelling the Generalisation Limits of Language Models for Biographical RE](https://mlsanity.com/paper/2505.12533/) (2025)
- [FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space](https://mlsanity.com/paper/2101.11212/) (2021)
- [Capturing Knowledge of Emerging Entities From Extended Search Snippets](https://mlsanity.com/paper/2104.01105/) (2021)
- [Cross-Lingual Fine-Grained Entity Typing](https://mlsanity.com/paper/2110.07837/) (2021)

## Abstract (arXiv)

In this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features. Our target tasks are entity typing and relation extraction. For the first noise type, we introduce multi-instance multi-label learning algorithms using neural network models, and apply them to fine-grained entity typing for the first time. This gives our models comparable performance with the state-of-the-art supervised approach which uses global embeddings of entities. For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction. Our experiments show that probabilistic predictions are more robust than discrete predictions and that joint training of the two tasks performs best.