Paper: The Infinite Tree

ACL ID P07-1035
Title The Infinite Tree
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007

Historically, unsupervised learning tech- niques have lacked a principled technique for selecting the number of unseen compo- nents. Research into non-parametric priors, such as the Dirichlet process, has enabled in- stead the use of infinite models, in which the number of hidden categories is not fixed, but can grow with the amount of training data. Here we develop the infinite tree, a new infi- nite model capable of representing recursive branching structure over an arbitrarily large set of hidden categories. Specifically, we develop three infinite tree models, each of which enforces different independence as- sumptions, and for each model we define a simple direct assignment sampling inference procedure.