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 focuses on addressing overlapped entity recognition. Compared to Flat Named Entity Recognition (FNER)%2C annotated resources are scarce in the corpus for NNER. Data augmentation is an effective approach to address the insufficient annotated corpus. However%2C there is a significant lack of exploration in data augmentation methods for NNER. Due to the presence of nested entities in NNER%2C existing data augmentation methods cannot be directly applied to NNER tasks. Therefore%2C in this work%2C we focus on data augmentation for NNER and resort to more expressive structures%2C Composited-Nested-Label Classification (CNLC) in which constituents are combined by nested-word and nested-label%2C to model nested entities. The dataset is augmented using the Composited-Nested-Learning (CNL). In addition%2C we propose the Confidence Filtering Mechanism (CFM) for a more efficient selection of generated data. Experimental results demonstrate that this approach results in improvements in ACE2004 and ACE2005 and alleviates the impact of sample imbalance.?quality=80&w=800)
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