This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semisupervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-ofthe-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.
Most recently, work by REF uses Adaptor Grammars for minimally supervised segmentation.
Minimally-Supervised Morphological Segmentation using Adaptor Grammars
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science