Semi-Supervised Learning for Natural Language Processing

  title={Semi-Supervised Learning for Natural Language Processing},
  author={John Blitzer and Xiaojin Zhu},
The amount of unlabeled linguistic data available to us is much larger and growing much faster than the amount of labeled data. Semi-supervised learning algorithms combine unlabeled data with a small labeled training set to train better models. This tutorial emphasizes practical applications of semisupervised learning; we treat semi-supervised learning methods as tools for building effective models from limited training data. An attendee will leave our tutorial with 1. A basic knowledge of the… CONTINUE READING