In this paper, we focus on named entity boundary detection, which is to detect the start and end boundaries of an entity mention in text, without predicting its type. The detected entities are input to entity linking or fine-grained typing systems for semantic enrichment. We propose BdryBot, a recurrent neural network encoder-decoder framework with a pointer network to detect entity boundaries from a given sentence. The encoder considers both character-level representations and word-level embeddings to represent the input words. In this way, BdryBot does not require any hand-crafted features. Because of the pointer network, BdryBot overcomes the problem of variable size output vocabulary and the issue of sparse boundary tags. We conduct two sets of experiments, in-domain detection and cross-domain detection, on six datasets. Our results show that BdryBot achieves state-of-the-art performance against five baselines. In addition, our proposed approach can be further enhanced when incorporating contextualized language embeddings into token representations.

中文翻译：

---

神经命名实体边界检测


在本文中，我们专注于命名实体边界检测，即检测文本中提及的实体的开始和结束边界，而不预测其类型。检测到的实体被输入到实体链接或细粒度类型系统以进行语义丰富。我们提出了 BdryBot，这是一种循环神经网络编码器-解码器框架，具有指针网络来检测给定句子中的实体边界。编码器考虑字符级表示和单词级嵌​​入来表示输入单词。这样，BdryBot 不需要任何手工制作的功能。由于指针网络，BdryBot克服了输出词汇大小可变的问题和边界标签稀疏的问题。我们在六个数据集上进行了两组实验，即域内检测和跨域检测。我们的结果表明，BdryBot 在五个基线上实现了最先进的性能。此外，当将上下文化语言嵌入合并到令牌表示中时，我们提出的方法可以进一步增强。