Data-Driven Graph Construction for Semi-Supervised Graph-Based Learning in NLP

Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natural language processing. All graph-based algorithms rely on a graph that jointly represents labeled and unlabeled data points. The problem of how to best construct this graph remains largely unsolved. In this paper we introduce a data-driven method that optimizes the representation of the initial feature space for graph construction by means of a supervised classifier. We apply this technique in the framework of label propagation and evaluate it on two different classification tasks, a multi-class lexicon acquisition task and a word sense disambiguation task. Significant improvements are demonstrated over both label propagation using conventional graph construction and state-of-the-art supervised classifiers.