Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation
The state of art natural language processing systems relies on sizable training datasets to achieve high performance. Lack of such datasets in the specialized low resource domains lead to suboptimal performance. In this work, we adapt backtranslation to generate high quality and linguistically diverse synthetic data for low-resource named entity recognition. We perform experiments on two datasets from the materials science (MaSciP) and biomedical domains (S800). The empirical results demonstrate the effectiveness of our proposed augmentation strategy, particularly in the low-resource scenario.
Code (1)
Tasks
Data AugmentationLow Resource Named Entity Recognitionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Similar Papers 제목 키워드 기반
Syntax-driven Data Augmentation for Named Entity Recognition
In low resource settings, data augmentation strategies are commonly leveraged to improve performance. Numerous approaches have attempted document-level augmentation (e.g., text classification), but few studies have explo…
Data AugmentationLanguage ModelingLanguage Modellingnamed-entity-recognition+4Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts
Data augmentation is important in addressing data sparsity and low resources in NLP. Unlike data augmentation for other tasks such as sentence-level and sentence-pair ones, data augmentation for named entity recognition …
Data Augmentationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2Composited-Nested-Learning with Data Augmentation for Nested Named Entity Recognition
Nested Named Entity Recognition (NNER) focuses on addressing overlapped entity recognition. Compared to Flat Named Entity Recognition (FNER), annotated resources are scarce in the corpus for NNER. Data augmentation is an…
Data Augmentationnamed-entity-recognitionNamed Entity RecognitionNested Named Entity RecognitionData Augmentation for Cross-Domain Named Entity Recognition
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where …
Cross-Domain Named Entity RecognitionData Augmentationnamed-entity-recognitionNamed Entity Recognition+2Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?
Named Entity Recognition(NER) for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available, and has been an area of increasing interest within NLP. Data a…
Data Augmentationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+1