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

ACL ID N07-1026
Title Data-Driven Graph Construction for Semi-Supervised Graph-Based Learning in NLP
Venue Human Language Technologies
Session Main Conference
Year 2007
Authors

Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natu- ral language processing. All graph-based algorithms rely on a graph that jointly rep- resents 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 construc- tion by means of a supervised classi er. We apply this technique in the frame- work of label propagation and evaluate it on two different classi cation tasks, a multi-class lexicon acquisition task and a word sense disambiguation task. Signi - cant improvements are demonstrated over both label propagation using conventional graph constructi...