# Local Additivity Based Data Augmentation for Semi-supervised NER

arXiv: https://arxiv.org/abs/2010.01677 · HTML: https://mlsanity.com/paper/2010.01677/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 30 of 31)

- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization](https://mlsanity.com/paper/2106.00149/) (2021)
- [FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning](https://mlsanity.com/paper/2108.06332/) (2021)
- [A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability](https://mlsanity.com/paper/2212.10888/) (2022)
- [Substructure Substitution: Structured Data Augmentation for NLP](https://mlsanity.com/paper/2101.00411/) (2021)
- [Label Semantics for Few Shot Named Entity Recognition](https://mlsanity.com/paper/2203.08985/) (2022)
- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [Rethinking Semi-supervised Learning with Language Models](https://mlsanity.com/paper/2305.13002/) (2023)
- [TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding](https://mlsanity.com/paper/2205.06153/) (2022)
- [To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP](https://mlsanity.com/paper/2111.09618/) (2021)
- [Information Extraction in Low-Resource Scenarios: Survey and Perspective](https://mlsanity.com/paper/2202.08063/) (2022)
- [LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification](https://mlsanity.com/paper/2402.16515/) (2024)
- [Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction](https://mlsanity.com/paper/2306.14122/) (2023)
- [Learning to Classify Open Intent via Soft Labeling and Manifold Mixup](https://mlsanity.com/paper/2204.07804/) (2022)
- [Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition](https://mlsanity.com/paper/2203.10693/) (2022)
- [Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting](https://mlsanity.com/paper/2204.11406/) (2022)
- [Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs](https://mlsanity.com/paper/2305.09608/) (2023)
- [Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?](https://mlsanity.com/paper/2407.02062/) (2024)
- [Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition](https://mlsanity.com/paper/2210.07916/) (2022)
- [Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation](https://mlsanity.com/paper/2305.15872/) (2023)
- [A Generalized Theory of Mixup for Structure-Preserving Synthetic Data](https://mlsanity.com/paper/2503.02645/) (2025)
- [Semi-supervised Relation Extraction via Data Augmentation and Consistency-training](https://mlsanity.com/paper/2306.10153/) (2023)
- [The Effects of Mixed Sample Data Augmentation are Class Dependent](https://mlsanity.com/paper/2307.09136/) (2023)
- [Test-Time Mixup Augmentation for Data and Class-Specific Uncertainty Estimation in Deep Learning Image Classification](https://mlsanity.com/paper/2212.00214/) (2022)
- [Enhancing Few-shot NER with Prompt Ordering based Data Augmentation](https://mlsanity.com/paper/2305.11791/) (2023)
- [Investigating Masking-based Data Generation in Language Models](https://mlsanity.com/paper/2307.00008/) (2023)
- [SegMix: A Simple Structure-Aware Data Augmentation Method](https://mlsanity.com/paper/2311.09505/) (2023)
- [ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition](https://mlsanity.com/paper/2403.17385/) (2024)

## Abstract (arXiv)

Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the dependence on labeled data, we propose a Local Additivity based Data Augmentation (LADA) method for semi-supervised NER, in which we create virtual samples by interpolating sequences close to each other. Our approach has two variations: Intra-LADA and Inter-LADA, where Intra-LADA performs interpolations among tokens within one sentence, and Inter-LADA samples different sentences to interpolate. Through linear additions between sampled training data, LADA creates an infinite amount of labeled data and improves both entity and context learning. We further extend LADA to the semi-supervised setting by designing a novel consistency loss for unlabeled data. Experiments conducted on two NER benchmarks demonstrate the effectiveness of our methods over several strong baselines. We have publicly released our code at https://github.com/GT-SALT/LADA.