mlsanityData to 7 Oct 2026

SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition

arXiv 2305.04076 ↗First posted 6 May 2023cs.CL cs.AIcited by 4

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Abstract (arXiv)

Distantly-Supervised Named Entity Recognition effectively alleviates the burden of time-consuming and expensive annotation in the supervised setting. But the context-free matching process and the limited coverage of knowledge bases introduce inaccurate and incomplete annotation noise respectively. Previous studies either considered only incomplete annotation noise or indiscriminately handle two types of noise with the same strategy. In this paper, we argue that the different causes of two types of noise bring up the requirement of different strategies in model architecture. Therefore, we propose the SANTA to handle these two types of noise separately with (1) Memory-smoothed Focal Loss and Entity-aware KNN to relieve the entity ambiguity problem caused by inaccurate annotation, and (2) Boundary Mixup to alleviate decision boundary shifting problem caused by incomplete annotation and a noise-tolerant loss to improve the robustness. Benefiting from our separate tailored strategies, we confirm in the experiment that the two types of noise are well mitigated. SANTA also achieves a new state-of-the-art on five public datasets.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 6 May 2023.

Builds on

25 of 45 references.

References · 25

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13 more
  1. 2019On Symmetric Losses for Learning from Corrupted Labelscited by 53
  2. 2019Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learningcited by 46
  3. 2021Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Trainingcited by 41
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  10. 2021Rethinking Negative Sampling for Handling Missing Entity Annotationscited by 12
  11. 2022SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Predictioncited by 12
  12. 2021Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learningcited by 11
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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 2.

4 papers.

  1. 2023MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learningcited by 171
  2. 2023Self-Distillation with Meta Learning for Knowledge Graph Completioncited by 1
  3. 2025Few Dimensions are Enough: Fine-tuning BERT with Selected Dimensions Revealed Its Redundant Naturecited by 1
  4. 2025Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotationscited by 0