Named entity recognition (NER) is one of the best studied tasks in natural
language processing. However, most approaches are not capable of handling
nested structures which are common in many applications. In this paper we
introduce a novel neural network architecture that first merges tokens and/or
entities into entities forming nested structures, and then labels each of them
independently. Unlike previous work, our merge and label approach predicts
real-valued instead of discrete segmentation structures, which allow it to
combine word and nested entity embeddings while maintaining differentiability.
%which smoothly groups entities into single vectors across multiple levels. We
evaluate our approach using the ACE 2005 Corpus, where it achieves
state-of-the-art F1 of 74.6, further improved with contextual embeddings (BERT)
to 82.4, an overall improvement of close to 8 F1 points over previous
approaches trained on the same data. Additionally we compare it against
BiLSTM-CRFs, the dominant approach for flat NER structures, demonstrating that
its ability to predict nested structures does not impact performance in simpler
cases.

我们介绍了一种基于神经网络架构的新型 NER 方法，可以对复杂语料中的嵌套结构准确地进行标注，与之前的方法不同，我们的方法采用预测实数形式的分段结构，从而能够在保持可微分性的同时，将单词和嵌套实体嵌入结合在一起。我们使用 ACE 2005 语料库对该方法进行评估，其 F1 得分为 74.6，使用上下文嵌入（BERT）可以进一步提高至 82.4，比同类数据训练上的其他方法提高近 8 F1 点。此外，我们将其与 BiLSTM-CRF 进行比较，并比较了它在简单情况下预测嵌套结构的能力对性能的影响。