Paper: Graph-Based Lexicon Expansion with Sparsity-Inducing Penalties

ACL ID N12-1086
Title Graph-Based Lexicon Expansion with Sparsity-Inducing Penalties
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012

We present novel methods to construct com- pact natural language lexicons within a graph- based semi-supervised learning framework, an attractive platform suited for propagating soft labels onto new natural language types from seed data. To achieve compactness, we induce sparse measures at graph vertices by incorporating sparsity-inducing penalties in Gaussian and entropic pairwise Markov networks constructed from labeled and unla- beled data. Sparse measures are desirable for high-dimensional multi-class learning prob- lems such as the induction of labels on natu- ral language types, which typically associate with only a few labels. Compared to standard graph-based learning methods, for two lexicon expansion problems, our approach produces significantly smaller lexicons and obtains bet- t...