Paper Citation Record · LEDGER

EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 42 inbound Pith citation observations for arXiv:1901.11196.

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

pith.paper-citation-record.v1
1901.11196 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 42 of 42 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:51:08.158794Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

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External citation measurements

198
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2fae0836-11fc-4ae8-b38a-5078b79b95b1 · inbound

Text and Code Embeddings by Contrastive Pre-Training cites this paper.

Text and Code Embeddings by Contrastive Pre-Training EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:24:12.104897Z

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Observation a83f4541-3d3c-4559-a662-f57c4137e08d · inbound

ART: Automatic multi-step reasoning and tool-use for large language models cites this paper.

ART: Automatic multi-step reasoning and tool-use for large language models EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T19:03:06.067623Z

Source-reported events for the cited work

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Observation 9973a1e1-1a7e-4873-b5cd-f824c26b7f68 · inbound

Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models cites this paper.

Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 39

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Observation 30d1e5c3-4a5e-4864-934d-cd21d5dac396 · inbound

Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search cites this paper.

Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 55

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Observation 7369487c-ddf7-4bde-ba15-31a62309a3aa · inbound

Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension Ability cites this paper.

Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension Ability EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 74

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Source-reported events for the cited work

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Observation f94d4079-9871-4239-ad56-a2f44d70195f · inbound

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline cites this paper.

Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 45

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Source-reported events for the cited work

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Observation dff76f85-c99a-4b7b-89d1-ff3a9da7a068 · inbound

Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data cites this paper.

Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 92

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Observation 6b4f3bc9-25b4-47d6-9063-04a4621eef35 · inbound

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies cites this paper.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 19

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Observation b527d9bf-42fb-4362-b7fc-48c93c33d6df · inbound

Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models cites this paper.

Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 36

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Observation ac0c5486-a3fc-4b3e-ae83-d7053eeacd70 · inbound

Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles: A Hierarchical Framework for Enhanced NLP Tasks cites this paper.

Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles: A Hierarchical Framework for Enhanced NLP Tasks EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 6

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Source-reported events for the cited work

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Observation 5b2e7366-14c3-4faa-88fa-17c30b7c5428 · inbound

Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities cites this paper.

Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 66

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Observation 888c4da3-a700-41a5-aa2a-8df7295b998c · inbound

A Large-Scale Benchmark for Vietnamese Sentence Paraphrases cites this paper.

A Large-Scale Benchmark for Vietnamese Sentence Paraphrases EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 2022

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Observation 2c6f0706-79b2-4a46-b5b1-f545bbaef59c · inbound

Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study cites this paper.

Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:57:22.850575Z

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Observation ae77f300-536b-4e93-8141-8eb35c251e3a · inbound

Less is More: Adaptive Coverage for Synthetic Training Data cites this paper.

Less is More: Adaptive Coverage for Synthetic Training Data EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 54

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Observation affdc4f9-7442-4e05-952f-1561aa92a292 · inbound

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks cites this paper.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 9

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Source-reported events for the cited work

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Observation 5ce674ab-26e8-468a-98ae-4315901872f9 · inbound

Advancing Scientific Text Classification: Fine-Tuned Models with Dataset Expansion and Hard-Voting cites this paper.

Advancing Scientific Text Classification: Fine-Tuned Models with Dataset Expansion and Hard-Voting EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 29

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Observation d2a0e527-a38b-4533-94ad-5b7b0c3ccad2 · inbound

Ustnlp16 at SemEval-2025 Task 9: Improving Model Performance through Imbalance Handling and Focal Loss cites this paper.

Ustnlp16 at SemEval-2025 Task 9: Improving Model Performance through Imbalance Handling and Focal Loss EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:45.489564Z

Source-reported events for the cited work

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Observation 0fa96a10-1a90-4503-9717-b30b1b1443b9 · inbound

Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models cites this paper.

Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 17

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Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models cites this paper.

Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 53

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Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs cites this paper.

Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 10

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Observation f7c97eea-c263-4b52-aeda-2d089288d7ff · inbound

3D Skeleton-Based Action Recognition: A Review cites this paper.

3D Skeleton-Based Action Recognition: A Review EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 81

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Observation 2b9ea115-1214-4048-b6e4-1eff698a8c29 · inbound

MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching cites this paper.

MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 8

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Observation ded7e81e-157e-4832-be5d-73889480a545 · inbound

Explainable AI: XAI-Guided Context-Aware Data Augmentation cites this paper.

Explainable AI: XAI-Guided Context-Aware Data Augmentation EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 7

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Observation 7ec3effb-2a4d-493b-9ab5-d78adffcce4d · inbound

Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis cites this paper.

Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 18

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Observation ef81e498-23a0-4969-9da0-72c9afe94548 · inbound

Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classification cites this paper.

Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classification EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 72

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Observation 2a2e483d-84ab-4a49-99ef-b9af3e100690 · inbound

Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models cites this paper.

Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:41:53.067455Z

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Observation d0c1ca68-9855-4778-b9c9-7425114a874e · inbound

Dual Enhancement on 3D Vision-Language Perception for Monocular 3D Visual Grounding cites this paper.

Dual Enhancement on 3D Vision-Language Perception for Monocular 3D Visual Grounding EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T15:59:18.910505Z

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Observation 1bfb0a6d-bfa1-413e-8a91-b1a4ef58bb5d · inbound

Intelligent Agents with Emotional Intelligence: Current Trends, Challenges, and Future Prospects cites this paper.

Intelligent Agents with Emotional Intelligence: Current Trends, Challenges, and Future Prospects EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 272

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T08:12:30.367117Z

Source-reported events for the cited work

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Observation c3ab6090-92bc-493c-8266-844397e9135c · inbound

inversedMixup: Data Augmentation via Inverting Mixed Embeddings cites this paper.

inversedMixup: Data Augmentation via Inverting Mixed Embeddings EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 26

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Observation c7c83851-1a58-476f-b86e-628122f20575 · inbound

What Are Adversaries Doing? Automating Tactics, Techniques, and Procedures Extraction: A Systematic Review cites this paper.

What Are Adversaries Doing? Automating Tactics, Techniques, and Procedures Extraction: A Systematic Review EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 160

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:53:23.592689Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 78f4fd42-e3c9-4a50-a66f-74b145e8b2cc · inbound

From Pre-trained Models to Large Language Models: A Comprehensive Survey of AI-Driven Psychological Computing cites this paper.

From Pre-trained Models to Large Language Models: A Comprehensive Survey of AI-Driven Psychological Computing EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T12:40:00.362002Z

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Observation 9964e32d-b46e-491c-b645-bb1c29477cc9 · inbound

Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations cites this paper.

Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T04:09:57.420810Z

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Observation d547aacf-2200-488c-acd1-f13e4438f0b1 · inbound

Model-Agnostic Meta Learning for Class Imbalance Adaptation cites this paper.

Model-Agnostic Meta Learning for Class Imbalance Adaptation EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 45

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Observation 37f898e9-1a87-414f-9240-03455ce844bd · inbound

Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions cites this paper.

Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 2

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Observation d8e4cd9f-4047-4928-8269-0b5d01fcc5b2 · inbound

ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation cites this paper.

ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:31:26.999284Z

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Observation e87c0e37-a589-4688-852e-6e2842bdec0d · inbound

ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search cites this paper.

ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 176

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Observation c4749833-0c9f-4b6a-b062-c02075baf4ac · inbound

ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search cites this paper.

ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 176

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:17:28.850075Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b35a08e7-1785-4cbd-938e-db41415d138c · inbound

Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems cites this paper.

Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 155

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:16:53.817430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9a4becef-3c80-4b16-a25e-f5c60f87b7e8 · inbound

Cross Paraphrastic Invariance Learning for Hallucination Detection cites this paper.

Cross Paraphrastic Invariance Learning for Hallucination Detection EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:57:23.742584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6329f739-0e19-4713-8c77-f37bab2cf260 · inbound

Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning cites this paper.

Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T05:17:40.235205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ee5e2a28-94fb-4c12-85c0-11da37894356 · inbound

Rethinking Text-to-Image as Semantic-Aware Data Augmentation for Indoor Scene Recognition cites this paper.

Rethinking Text-to-Image as Semantic-Aware Data Augmentation for Indoor Scene Recognition EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T00:09:14.176566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8cd5f321-7711-48a2-9cb3-a02f26eee313 · inbound

Multilingual Polarization Detection Using Transformer-Based Models with Class Weighting and Threshold Tuning cites this paper.

Multilingual Polarization Detection Using Transformer-Based Models with Class Weighting and Threshold Tuning EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T12:25:43.535957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.