In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions.

翻译：本文综述了主动学习在自然语言处理中的应用。除了对查询策略进行细粒度分类外，我们还研究了将主动学习应用于NLP问题的其他几个重要方面，包括结构化预测任务中的主动学习、标注成本、模型学习（特别是深度神经网络模型）以及主动学习的启动与停止。最后，我们讨论了相关主题和未来方向。