A Survey of Active Learning for Natural Language Processing
It provides a comprehensive review for researchers and practitioners in NLP, but is incremental as a survey paper.
This paper surveys active learning techniques for natural language processing, categorizing query strategies and investigating aspects like structured prediction, annotation costs, and model learning with deep neural networks.
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.