部分类型化的NER数据集集成：实践与理论的结合
Partially-Typed NER Datasets Integration: Connecting Practice to Theory
摘要：
虽然典型的命名实体识别（NER）模型需要用所有目标类型来注释训练集，但每个可用的数据集可能仅覆盖其中的一部分。与依赖于完全类型化的NER数据集不同，人们已经做出了许多努力来利用多个部分类型化的数据集进行训练，并允许生成的模型覆盖完整的类型集。然而，既没有保证集成数据集的质量，也没有指导训练算法的设计。在这里，我们进行了系统的分析和比较部分类型的NER数据集和全类型的，在理论和实证的方式。首先，我们推导出一个界限，以建立部分类型化的注释训练的模型可以达到与完全类型化的注释训练的模型相似的性能，这也提供了算法设计的指导。此外，我们进行了对照实验，结果表明部分类型化数据集与使用相同数量的全类型化注释训练的模型具有相似的性能
英文摘要：
While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them. Instead of relying on fully-typed NER datasets, many efforts have been made to leverage multiple partially-typed ones for training and allow the resulting model to cover a full type set. However, there is neither guarantee on the quality of integrated datasets, nor guidance on the design of training algorithms. Here, we conduct a systematic analysis and comparison between partially-typed NER datasets and fully-typed ones, in both theoretical and empirical manner. Firstly, we derive a bound to establish that models trained with partially-typed annotations can reach a similar performance with the ones trained with fully-typed annotations, which also provides guidance on the algorithm design. Moreover, we conduct controlled experiments, which shows partially-typed datasets leads to similar performance with the model trained with the same amount of fully-typed annotations
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
