Simple yet effective data augmentation techniques have been proposed for
sentence-level and sentence-pair natural language processing tasks. Inspired by
these efforts, we design and compare data augmentation for named entity
recognition, which is usually modeled as a token-level sequence labeling
problem. Through experiments on two data sets from the biomedical and materials
science domains (i2b2-2010 and MaSciP), we show that simple augmentation can
boost performance for both recurrent and transformer-based models, especially
for small training sets.

本研究针对基于标记序列的命名实体识别任务，设计并比较了数据增强技术的效果。通过在生物医学和材料科学领域的两个数据集（i2b2-2010 和 MaSciP）上进行实验，我们表明简单的增强技术可以提高循环模型和基于 Transformer 模型的性能，尤其是对于小训练集的情况。

命名实体识别简单数据增强分析

An Analysis of Simple Data Augmentation for Named Entity Recognition

When training data is sparse, more domain knowledge must be incorporated into
the learning algorithm in order to reduce the effective size of the hypothesis
space. This paper builds on previous work in which knowledge about qualitative
monotonicities was formally represented and incorporated into learning
algorithms (e.g., Clark & Matwin's work with the CN2 rule learning algorithm).
We show how to interpret knowledge of qualitative influences, and in particular
of monotonicities, as constraints on probability distributions, and to
incorporate this knowledge into Bayesian network learning algorithms. We show
that this yields improved accuracy, particularly with very small training sets
(e.g. less than 10 examples).

本文通过将定性单调性知识表现为概率分布约束，并将其纳入贝叶斯网络学习算法，从而改善在小规模数据训练下的准确度提升。