Named Entity Recognition (NER) is a fundamental task in the fields of natural language processing and information extraction. NER has been widely used as a standalone tool or an essential component in a variety of applications such as question answering, dialogue assistants and knowledge graphs development. However, training reliable NER models requires a large amount of labelled data which is expensive to obtain, particularly in specialized domains. This paper describes a method to learn a domain-specific NER model for an arbitrary set of named entities when domain-specific supervision is not available. We assume that the supervision can be obtained with no human effort, and neural models can learn from each other. The code, data and models are publicly available.

中文翻译：

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命名实体识别任务的域可转移方法

命名实体识别（NER）是自然语言处理和信息提取领域中的一项基本任务。NER已被广泛用作独立工具或各种应用程序中的基本组件，例如问题解答，对话助手和知识图谱开发。但是，训练可靠的NER模型需要大量的标记数据，而这些数据的获取成本很高，尤其是在专业领域。本文介绍了一种方法，当没有特定于域的监督时，该方法可为任意一组命名实体学习特定于域的NER模型。我们假设可以无需人工就能获得监督，并且神经模型可以相互学习。代码，数据和模型是公开可用的。