论文标题
命名实体识别任务的域转移方法
Domain-Transferable Method for Named Entity Recognition Task
论文作者
论文摘要
命名实体识别（NER）是自然语言处理和信息提取领域的基本任务。 NER已被广泛用作独立工具或在各种应用中的重要组成部分，例如问答，对话助手和知识图的开发。但是，培训可靠的NER模型需要大量标记的数据，这些数据很昂贵，尤其是在专用域中。本文介绍了一种在没有特定于域特定的监督的情况下，学习一组命名实体的特定于域的NER模型的方法。我们假设可以在没有人为努力的情况下获得监督，而神经模型可以互相学习。代码，数据和模型公开可用。
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.
