BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER
Biomedical Named Entity Recognition (BioNER) is the fundamental task of identifying named entities from biomedical text. However, BioNER suffers from severe data scarcity and lacks high-quality labeled data due to the highly specialized and expert knowledge required for annotation. Though data augmentation has shown to be highly effective for low-resource NER in general, existing data augmentation techniques fail to produce factual and diverse augmentations for BioNER. In this paper, we present BioAug, a novel data augmentation framework for low-resource BioNER. BioAug, built on BART, is trained to solve a novel text reconstruction task based on selective masking and knowledge augmentation. Post training, we perform conditional generation and generate diverse augmentations conditioning BioAug on selectively corrupted text similar to the training stage. We demonstrate the effectiveness of BioAug on 5 benchmark BioNER datasets and show that BioAug outperforms all our baselines by a significant margin (1.5%-21.5% absolute improvement) and is able to generate augmentations that are both more factual and diverse. Code: https://github.com/Sreyan88/BioAug.
Code (1)
Tasks
Data Augmentationnamed-entity-recognitionNamed Entity RecognitionNERMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
RL-BioAug: Label-Efficient Reinforcement Learning for Self-Supervised EEG Representation Learning
The quality of data augmentation serves as a critical determinant for the performance of contrastive learning in EEG tasks. Although this paradigm is promising for utilizing unlabeled data, static or random augmentation …
Representation LearningReinforcement LearningContrastive LearningSeizure DetectionData Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which aug…
Data Augmentationreinforcement-learningReinforcement Learning (RL)Text Augmentation+2Targeted Augmentation for Low-Resource Event Extraction
Addressing the challenge of low-resource information extraction remains an ongoing issue due to the inherent information scarcity within limited training examples. Existing data augmentation methods, considered potential…
Data AugmentationDiversityEvent ExtractionACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM Attention-map aware keyword selection for Conditional Language Mod…
Data AugmentationDenoisingLanguage Modellingnamed-entity-recognition+4DALE: Generative Data Augmentation for Low-Resource Legal NLP
We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP. DALE addresses the challenges existing frameworks pose in generating effective data augmentations of legal documen…
Data AugmentationDecoderDenoisingLanguage Modeling+2