Learning from labeled features using generalized expectation criteria

It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domain knowledge in the form of affinities between input features and classes. For example, in a baseball vs. hockey text classification problem, even without any labeled data, we know that the presence of the word puck is a strong indicator of hockey. We refer to this type of domain knowledge as a labeled feature. In this paper, we propose a method for training discriminative probabilistic models with labeled features and unlabeled instances. Unlike previous approaches that use labeled features to create labeled pseudo-instances, we use labeled features directly to constrain the model's predictions on unlabeled instances. We express these soft constraints using generalized expectation (GE) criteria -terms in a parameter estimation objective function that express preferences on values of a model expectation. ...

• Type: Journal
• Year: 2008
• Where: SIGIR
• Authors: Gregory Druck, Gideon S. Mann, Andrew McCallum
• Keywords: Discriminative Probabilistic Models, Information Technology, Labeled Features, SIGIR 2008, Text Classification