When an entity name contains other names within it, the identification of all
combinations of names can become difficult and expensive. We propose a new
method to recognize not only outermost named entities but also inner nested
ones. We design an objective function for training a neural model that treats
the tag sequence for nested entities as the second best path within the span of
their parent entity. In addition, we provide the decoding method for inference
that extracts entities iteratively from outermost ones to inner ones in an
outside-to-inside way. Our method has no additional hyperparameters to the
conditional random field based model widely used for flat named entity
recognition tasks. Experiments demonstrate that our method performs better than
or at least as well as existing methods capable of handling nested entities,
achieving the F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005,
and GENIA datasets, respectively.

本文提出了一种新的方法来识别包含嵌套实体的实体名，该方法使用具有嵌套实体标签序列的神经模型设计客观函数进行训练，并提供了推断的解码方法，该方法无需额外的超参数，实验表明与现有的处理嵌套实体的方法相比，该方法表现更好，达到了 ACE-2004、ACE-2005 和 GENIA 数据集的 F1 分数分别为 85.82%、84.34% 和 77.36%。