Semi-Supervised Modeling for Prenominal Modifier Ordering
Margaret A. Mitchell, Aaron Dunlop, Brian Roark · 2011
In this paper, we argue that ordering prenom-inal modifiers – typically pursued as a su-pervised modeling task – is particularly well-suited to semi-supervised approaches. By relying on automatic parses to extract noun phrases, we can scale up the training data by orders of magnitude. This minimizes the predominant issue of data sparsity that has informed most previous approaches. We compare several recent approaches, and find improvements from additional training data across the board; however, none outperform a simple n-gram model. 1