# Learning to Denoise Distantly-Labeled Data for Entity Typing

arXiv: https://arxiv.org/abs/1905.01566 · HTML: https://mlsanity.com/paper/1905.01566/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2019.

## Built on by (most cited first, 24 of 24)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Prompt-Learning for Fine-Grained Entity Typing](https://mlsanity.com/paper/2108.10604/) (2021)
- [Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training](https://mlsanity.com/paper/2109.05003/) (2021)
- [Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model](https://mlsanity.com/paper/2106.04098/) (2021)
- [Modeling Fine-Grained Entity Types with Box Embeddings](https://mlsanity.com/paper/2101.00345/) (2021)
- [LOME: Large Ontology Multilingual Extraction](https://mlsanity.com/paper/2101.12175/) (2021)
- [Fine-grained Entity Typing via Label Reasoning](https://mlsanity.com/paper/2109.05744/) (2021)
- [Unified Semantic Typing with Meaningful Label Inference](https://mlsanity.com/paper/2205.01826/) (2022)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference](https://mlsanity.com/paper/2202.06167/) (2022)
- [Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing](https://mlsanity.com/paper/2205.12640/) (2022)
- [Generative Entity Typing with Curriculum Learning](https://mlsanity.com/paper/2210.02914/) (2022)
- [Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy](https://mlsanity.com/paper/2305.12802/) (2023)
- [Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing](https://mlsanity.com/paper/2311.00835/) (2023)
- [Biomedical Interpretable Entity Representations](https://mlsanity.com/paper/2106.09502/) (2021)
- [Learning to Select from Multiple Options](https://mlsanity.com/paper/2212.00301/) (2022)
- [Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field](https://mlsanity.com/paper/2212.01581/) (2022)
- [Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing](https://mlsanity.com/paper/2212.09125/) (2022)
- [EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains](https://mlsanity.com/paper/2305.12924/) (2023)
- [From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained](https://mlsanity.com/paper/2312.06188/) (2023)
- [When in Doubt: Improving Classification Performance with Alternating Normalization](https://mlsanity.com/paper/2109.13449/) (2021)
- [Denoising Enhanced Distantly Supervised Ultrafine Entity Typing](https://mlsanity.com/paper/2210.09599/) (2022)
- [Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning](https://mlsanity.com/paper/2310.14596/) (2023)
- [Domain Adaptation of Multilingual Semantic Search -- Literature Review](https://mlsanity.com/paper/2402.02932/) (2024)

## Abstract (arXiv)

Distantly-labeled data can be used to scale up training of statistical models, but it is typically noisy and that noise can vary with the distant labeling technique. In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and denoised distant data with standard supervised training. Our denoising approach consists of two parts. First, a filtering function discards examples from the distantly labeled data that are wholly unusable. Second, a relabeling function repairs noisy labels for the retained examples. Each of these components is a model trained on synthetically-noised examples generated from a small manually-labeled set. We investigate this approach on the ultra-fine entity typing task of Choi et al. (2018). Our baseline model is an extension of their model with pre-trained ELMo representations, which already achieves state-of-the-art performance. Adding distant data that has been denoised with our learned models gives further performance gains over this base model, outperforming models trained on raw distant data or heuristically-denoised distant data.