# Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning arXiv: https://arxiv.org/abs/2204.09589 ยท HTML: https://mlsanity.com/paper/2204.09589/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 32 references that are in the map. - First posted on arXiv 3 Mar 2022. ## Builds on (most cited first, 20 of 20) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Rethinking Pre-training and Self-training](https://mlsanity.com/paper/2006.06882/) (2020) - [Positive-Unlabeled Learning with Non-Negative Risk Estimator](https://mlsanity.com/paper/1703.00593/) (2017) - [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018) - [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015) - [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020) - [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020) - [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://mlsanity.com/paper/2105.03654/) (2021) - [Learning Named Entity Tagger using Domain-Specific Dictionary](https://mlsanity.com/paper/1809.03599/) (2018) - [Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning](https://mlsanity.com/paper/1906.01378/) (2019) - [Classification from Positive, Unlabeled and Biased Negative Data](https://mlsanity.com/paper/1810.00846/) (2018) - [Low-Resource Name Tagging Learned with Weakly Labeled Data](https://mlsanity.com/paper/1908.09659/) (2019) - [Noisy-Labeled NER with Confidence Estimation](https://mlsanity.com/paper/2104.04318/) (2021) - [Named Entity Recognition with Partially Annotated Training Data](https://mlsanity.com/paper/1909.09270/) (2019) - [Estimating the class prior and posterior from noisy positives and unlabeled data](https://mlsanity.com/paper/1606.08561/) (2016) - [Scientific Information Extraction with Semi-supervised Neural Tagging](https://mlsanity.com/paper/1708.06075/) (2017) - [De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention](https://mlsanity.com/paper/2106.09233/) (2021) - [Lexicon Infused Phrase Embeddings for Named Entity Resolution](https://mlsanity.com/paper/1404.5367/) (2014) - [Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets](https://mlsanity.com/paper/1709.05094/) (2017) - [End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models](https://mlsanity.com/paper/1912.13415/) (2019) ## Built on by (most cited first, 21 of 21) - [Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.14565/) (2022) - [Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble](https://mlsanity.com/paper/2212.06522/) (2022) - [Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style](https://mlsanity.com/paper/2409.10955/) (2024) - [A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling](https://mlsanity.com/paper/2210.08709/) (2022) - [Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach](https://mlsanity.com/paper/2402.14948/) (2024) - [SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition](https://mlsanity.com/paper/2305.04076/) (2023) - [A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling](https://mlsanity.com/paper/2306.14806/) (2023) - [MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition](https://mlsanity.com/paper/2310.08298/) (2023) - [Improving Distantly Supervised Relation Extraction by Natural Language Inference](https://mlsanity.com/paper/2208.00346/) (2022) - [Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling](https://mlsanity.com/paper/2302.08659/) (2023) - [Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization](https://mlsanity.com/paper/2305.13066/) (2023) - [Better Sampling of Negatives for Distantly Supervised Named Entity Recognition](https://mlsanity.com/paper/2305.13142/) (2023) - [Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2306.09519/) (2023) - [DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition](https://mlsanity.com/paper/2504.04616/) (2025) - [Few Dimensions are Enough: Fine-tuning BERT with Selected Dimensions Revealed Its Redundant Nature](https://mlsanity.com/paper/2504.04966/) (2025) - [Do Not Harm Protected Groups in Debiasing Language Representation Models](https://mlsanity.com/paper/2310.18458/) (2023) - [ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition](https://mlsanity.com/paper/2403.17385/) (2024) - [Bridge: A Unified Framework to Knowledge Graph Completion via Language Models and Knowledge Representation](https://mlsanity.com/paper/2411.06660/) (2024) - [Learning from Concealed Labels](https://mlsanity.com/paper/2412.02230/) (2024) - [ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning](https://mlsanity.com/paper/2412.02240/) (2024) - [Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations](https://mlsanity.com/paper/2505.12454/) (2025) ## Abstract (arXiv) In this paper, we study the named entity recognition (NER) problem under distant supervision. Due to the incompleteness of the external dictionaries and/or knowledge bases, such distantly annotated training data usually suffer from a high false negative rate. To this end, we formulate the Distantly Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU) learning and propose a theoretically and practically novel CONFidence-based MPU (Conf-MPU) approach. To handle the incomplete annotations, Conf-MPU consists of two steps. First, a confidence score is estimated for each token of being an entity token. Then, the proposed Conf-MPU risk estimation is applied to train a multi-class classifier for the NER task. Thorough experiments on two benchmark datasets labeled by various external knowledge demonstrate the superiority of the proposed Conf-MPU over existing DS-NER methods.