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.
REF proposed Adaptor Grammars (AGMorph), a nonparametric Bayesian modelling framework for minimally supervised learning of morphological segmentation.
Minimally-Supervised Morphological Segmentation using Adaptor Grammars
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science