Title: Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning,
Abstract: We propose a general approach to modeling semi-supervised learning (SSL)
algorithms. Specifically, we present a declarative language for modeling both
traditional supervised classification tasks and many SSL heuristics, including
both well-known heuristics such as co-training and novel domain-specific
heuristics. In addition to representing individual SSL heuristics, we show that
multiple heuristics can be automatically combined using Bayesian optimization
methods. We experiment with two classes of tasks, link-based text
classification and relation extraction. We show modest improvements on
well-studied link-based classification benchmarks, and state-of-the-art results
on relation-extraction tasks for two realistic domains.