Paper: Concavity and Initialization for Unsupervised Dependency Parsing

ACL ID N12-1069
Title Concavity and Initialization for Unsupervised Dependency Parsing
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012
Authors

We investigate models for unsupervised learn- ing with concave log-likelihood functions. We begin with the most well-known example, IBM Model 1 for word alignment (Brown et al., 1993) and analyze its properties, dis- cussing why other models for unsupervised learning are so seldom concave. We then present concave models for dependency gram- mar induction and validate them experimen- tally. We find our concave models to be effec- tive initializers for the dependency model of Klein and Manning (2004) and show that we can encode linguistic knowledge in them for improved performance.