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:
this https URL

本文提出了一种基于 BART 的数据增强框架 BioAug，通过选择性遮掩和知识增强，训练 BioAug 以解决一种新颖的文本重构任务，并在 5 个基准 BioNER 数据集上证明了 BioAug 的有效性和优越性，能够生成更加真实和多样化的数据。

BioAug：基于条件生成的数据增强方法应用于低资源生物医学命名实体识别任务

BioAug: Conditional Generation based Data Augmentation for Low-Resource  Biomedical NER

Data augmentation is an effective solution to data scarcity in low-resource
scenarios. However, when applied to token-level tasks such as NER, data
augmentation methods often suffer from token-label misalignment, which leads to
unsatsifactory performance. In this work, we propose Masked Entity Language
Modeling (MELM) as a novel data augmentation framework for low-resource NER. To
alleviate the token-label misalignment issue, we explicitly inject NER labels
into sentence context, and thus the fine-tuned MELM is able to predict masked
entity tokens by explicitly conditioning on their labels. Thereby, MELM
generates high-quality augmented data with novel entities, which provides rich
entity regularity knowledge and boosts NER performance. When training data from
multiple languages are available, we also integrate MELM with code-mixing for
further improvement. We demonstrate the effectiveness of MELM on monolingual,
cross-lingual and multilingual NER across various low-resource levels.
Experimental results show that our MELM presents substantial improvement over
the baseline methods.

使用 MELM 作为一种新的数据增强框架，通过显式条件化于标签来预测掩码实体标记，从而为低资源 NER 生成高质量的增强数据，并通过与混合代码结合进一步提高了 MELM 的有效性，在单语，跨语言和多语言 NER 方面均取得了实验结果的显著改进。

MELM: 低资源命名实体识别的掩码实体语言建模数据增强

MELM: Data Augmentation with Masked Entity Language Modeling for  Low-Resource NER

Manually labeled corpora are expensive to create and often not available for
low-resource languages or domains. Automatic labeling approaches are an
alternative way to obtain labeled data in a quicker and cheaper way. However,
these labels often contain more errors which can deteriorate a classifier's
performance when trained on this data. We propose a noise layer that is added
to a neural network architecture. This allows modeling the noise and train on a
combination of clean and noisy data. We show that in a low-resource NER task we
can improve performance by up to 35% by using additional, noisy data and
handling the noise.

通过添加噪声层到神经网络结构中，我们可以处理噪声并联合清洁和嘈杂数据进行训练，从而提高低资源 NER 任务的性能，最多可提高 35%。