Margin-based active learning for structured predictions

Kevin Small, Dan Roth

Research output: Contribution to journalArticlepeer-review

Abstract

Margin-based active learning remains the most widely used active learning paradigm due to its simplicity and empirical successes. However, most works are limited to binary or multiclass prediction problems, thus restricting the applicability of these approaches to many complex prediction problems where active learning would be most useful. For example, machine learning techniques for natural language processing applications often require combining multiple interdependent prediction problems-generally referred to as learning in structured output spaces. In many such application domains, complexity is further managed by decomposing a complex prediction into a sequence of predictions where earlier predictions are used as input to later predictions-commonly referred to as a pipeline model. This work describes methods for extending existing margin-based active learning techniques to these two settings, thus increasing the scope of problems for which active learning can be applied. We empirically validate these proposed active learning techniques by reducing the annotated data requirements on multiple instances of synthetic data, a semantic role labeling task, and a named entity and relation extraction system.

Original languageEnglish (US)
Pages (from-to)3-25
Number of pages23
JournalInternational Journal of Machine Learning and Cybernetics
Volume1
Issue number1-4
DOIs
StatePublished - Dec 1 2010

Keywords

  • Active learning
  • Pipeline models
  • Structured output spaces
  • Structured predictions

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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