Paper Citation Record · LEDGER

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations

As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.12454.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.12454 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:33.864818Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 35e727f9-53b3-4a6a-affb-e1654dc3028e · outbound

This paper cites A Survey on Recent Advances in Named Entity Recognition from Deep Learning models.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations A Survey on Recent Advances in Named Entity Recognition from Deep Learning models

Reference 1

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Observation 5a301142-6cad-467f-a1d2-e0053d2e37af · outbound

This paper cites A survey on deep learning for named entity recognition,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations A survey on deep learning for named entity recognition,

Reference 2

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Observation ae8a6324-e1ec-4910-af00-ce17ae40b726 · outbound

This paper cites Bidirectional LSTM-CRF Models for Sequence Tagging.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Bidirectional LSTM-CRF Models for Sequence Tagging

Reference 3

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Observation 5a3b3bda-1c30-40e1-8368-aee95a23a912 · outbound

This paper cites Fast and Accurate Entity Recognition with Iterated Dilated Convolutions.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

Reference 4

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 5

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Observation 80b23c09-3304-4c0b-b89a-a416d5c44592 · outbound

This paper cites Universalner: Targeted distillation from large language models for open named entity recognition,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Universalner: Targeted distillation from large language models for open named entity recognition,

Reference 6

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Observation bd9cef87-1ff9-49f8-9efe-4d42975c5e2f · outbound

This paper cites Relation extraction using distant supervision: A survey,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Relation extraction using distant supervision: A survey,

Reference 7

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Observation 878c3fbb-ef41-4574-af1f-384fafbaba5a · outbound

This paper cites Bond: Bert-assisted open-domain named entity recognition with distant supervision,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Bond: Bert-assisted open-domain named entity recognition with distant supervision,

Reference 8

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Observation d2ffd7ca-6cae-43f5-afc2-64774f70ce7e · outbound

This paper cites Is GPT-3 a Good Data Annotator?.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Is GPT-3 a Good Data Annotator?

Reference 9

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Observation 7455e0f3-70b6-4542-bfa7-41d990df5ac0 · outbound

This paper cites Learning Named Entity Tagger using Domain-Specific Dictionary.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Learning Named Entity Tagger using Domain-Specific Dictionary

Reference 10

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Observation 5f79e7e5-c19c-4253-a5e1-8f0e45cc0290 · outbound

This paper cites Dual t: Reducing estimation error for transition matrix in label- noise learning,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Dual t: Reducing estimation error for transition matrix in label- noise learning,

Reference 11

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Observation 58042dca-ca98-4924-ae10-d9a05566e2da · outbound

This paper cites Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning

Reference 12

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Observation 66d8aaef-f0d3-4f7f-be3e-13d882586561 · outbound

This paper cites Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning

Reference 13

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This paper cites Learning from positive and unlabeled exam- ples with different data distributions,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Learning from positive and unlabeled exam- ples with different data distributions,

Reference 14

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Observation 9f4891d7-0811-4f46-a78c-7f77a3d17269 · outbound

This paper cites Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition

Reference 15

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Observation b3baa915-6e03-47cb-bbb3-fbcba6c243f6 · outbound

This paper cites Rethinking negative sampling for handling missing entity annotations,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Rethinking negative sampling for handling missing entity annotations,

Reference 16

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Observation d71cf8dc-e26a-415d-9117-6ccea4217ad7 · outbound

This paper cites Better sampling of negatives for distantly supervised named entity recognition.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Better sampling of negatives for distantly supervised named entity recognition

Reference 17

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Observation f2c09628-d339-4b91-a0e3-b4ae47ec4f59 · outbound

This paper cites Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning

Reference 18

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This paper cites An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods

Reference 19

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This paper cites Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction

Reference 20

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Observation 432c905f-0947-4e77-9a31-3ff3a76ef9fc · outbound

This paper cites Distantly supervised named entity recognition with spy-pu algorithm,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Distantly supervised named entity recognition with spy-pu algorithm,

Reference 21

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Class-imbalanced- aware distantly supervised named entity recognition,

Reference 22

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Boundary smoothing for named entity recogni- tion,

Reference 23

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Observation 5b6ff91d-17a2-4991-a97f-4ac8d344dfd5 · outbound

This paper cites SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER

Reference 24

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

Reference 25

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This paper cites Distantly su- pervised ner with partial annotation learning and reinforcement learning,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Distantly su- pervised ner with partial annotation learning and reinforcement learning,

Reference 26

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Observation bf809f2d-0ead-4533-b8d8-76c974bf8612 · outbound

This paper cites De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention

Reference 27

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This paper cites Santa: Separate strate- gies for inaccurate and incomplete annotation noise in distantly- supervised named entity recognition.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Santa: Separate strate- gies for inaccurate and incomplete annotation noise in distantly- supervised named entity recognition

Reference 28

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This paper cites Is ChatGPT a General-Purpose Natural Language Processing Task Solver?.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Reference 29

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This paper cites Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

Reference 30

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This paper cites PromptNER: Prompting For Named Entity Recognition.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations PromptNER: Prompting For Named Entity Recognition

Reference 31

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations GPT-NER: Named Entity Recognition via Large Language Models

Reference 32

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This paper cites Instructuie: Multi-task instruction tun- ing for unified information extraction.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Instructuie: Multi-task instruction tun- ing for unified information extraction

Reference 33

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Rethinking Negative Instances for Generative Named Entity Recognition

Reference 34

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This paper cites Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

Reference 35

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Design challenges and misconceptions in named entity recognition,

Reference 36

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This paper cites Biocreative v cdr task corpus: a resource for chemical disease relation extraction,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Biocreative v cdr task corpus: a resource for chemical disease relation extraction,

Reference 37

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This paper cites Training language models to follow instructions with human feedback,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Training language models to follow instructions with human feedback,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:34.756073Z

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations LLaMA: Open and Efficient Foundation Language Models

Reference 39

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Early-learning regularization prevents memorization of noisy labels,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:34.738898Z

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This paper cites How does disagreement help generalization against label corruption?.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations How does disagreement help generalization against label corruption?

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:34.722698Z

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This paper cites SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training

Reference 42

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Confident learning: Esti- mating uncertainty in dataset labels,

Reference 43

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This paper cites Asgard: A portable architecture for multilingual dialogue systems,.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Asgard: A portable architecture for multilingual dialogue systems,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:34.511804Z

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 45

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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations Decoupled Weight Decay Regularization

Reference 46

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This paper cites His research interests include spatial/text/graph data management, query opti- mization and data mining.

Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations His research interests include spatial/text/graph data management, query opti- mization and data mining

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:34.495721Z

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Pith citing papers

No inbound Pith citation observations are available.