The task to recognize named entities is often modeled as a sequence labeling process, which selects a label path whose probability is maximum for an input sentence. Because it makes the assumption that the input sentence has a flattened structure, it often fails to recognize nested named entities. In our previous work, a boundary assembling (BA) model was proposed. It is a cascading framework, which identifies named entity boundaries first, and then assembles them into entity candidates for further assessment. This model is effective to recognize nested named entities, but still suffers from poor performance caused by the sparse feature problem. In this article, the BA model is remodeled with the advancement of neural networks, which enables the model to capture semantic information of a sentence by using word embeddings pretrained in external resources. In our experiments, it shows an impressive improvement on the final performance, outperforming the state of the art more than 17% in F-score.

中文翻译：

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基于神经网络边界组装模型的嵌套命名实体识别

识别命名实体的任务通常被建模为序列标记过程，该过程为输入句子选择概率最大的标签路径。因为它假设输入句子具有扁平结构，所以它经常无法识别嵌套的命名实体。在我们之前的工作中，提出了边界组装（BA）模型。它是一个级联框架，它首先识别命名实体边界，然后将它们组合成实体候选以供进一步评估。该模型对于识别嵌套命名实体是有效的，但仍然存在稀疏特征问题导致的性能不佳的问题。在本文中，BA模型随着神经网络的进步进行了改造，这使模型能够通过使用在外部资源中预训练的词嵌入来捕获句子的语义信息。在我们的实验中，它显示了最终性能的显着改进，在 F 分数上超过了最先进的 17%。