A Survey of Active Learning for Natural Language Processing
Zhisong Zhang, Emma Strubell, Eduard Hovy
Abstract
In this work, we provide a literature review 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.
Topics & Concepts
Computer scienceCategorizationArtificial intelligenceNatural language processingDeep learningAnnotationNatural languageActive learning (machine learning)Machine learningMachine Learning and AlgorithmsNatural Language Processing TechniquesTopic Modeling