%20focuses%20on%20addressing%20overlapped%20entity%20recognition.%20Compared%20to%20Flat%20Named%20Entity%20Recognition%20(FNER)%2C%20annotated%20resources%20are%20scarce%20in%20the%20corpus%20for%20NNER.%20Data%20augmentation%20is%20an%20effective%20approach%20to%20address%20the%20insufficient%20annotated%20corpus.%20However%2C%20there%20is%20a%20significant%20lack%20of%20exploration%20in%20data%20augmentation%20methods%20for%20NNER.%20Due%20to%20the%20presence%20of%20nested%20entities%20in%20NNER%2C%20existing%20data%20augmentation%20methods%20cannot%20be%20directly%20applied%20to%20NNER%20tasks.%20Therefore%2C%20in%20this%20work%2C%20we%20focus%20on%20data%20augmentation%20for%20NNER%20and%20resort%20to%20more%20expressive%20structures%2C%20Composited-Nested-Label%20Classification%20(CNLC)%20in%20which%20constituents%20are%20combined%20by%20nested-word%20and%20nested-label%2C%20to%20model%20nested%20entities.%20The%20dataset%20is%20augmented%20using%20the%20Composited-Nested-Learning%20(CNL).%20In%20addition%2C%20we%20propose%20the%20Confidence%20Filtering%20Mechanism%20(CFM)%20for%20a%20more%20efficient%20selection%20of%20generated%20data.%20Experimental%20results%20demonstrate%20that%20this%20approach%20results%20in%20improvements%20in%20ACE2004%20and%20ACE2005%20and%20alleviates%20the%20impact%20of%20sample%20imbalance.)
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