Adaptor Grammars

Strong indications of perspective can often come from collocations of arbitrary length; for example, someone writing get the government out of my X is typically expressing a conserva- tive rather …...

This paper establishes a connection be- tween two apparently very different kinds of probabilistic models. Latent Dirich- let Allocation (LDA) models are used as “topic models” to produce a low- …...

Adaptor grammars are a framework for expressing and performing inference over a variety of non-parametric linguistic models. These models currently provide state-of-the-art performance on unsuper- vised word segmentation from phonemic …...

This paper contributes an approach for expressing non-concatenative morphological phenomena, such as stem derivation Semitic languages, in terms of a mildlyin context-sensitive grammar formalism. This offers a convenient level of …...

AbstractThis paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel …...

AbstractWe investigate using Adaptor Grammars for unsupervised morphological segmentation. Using six development languages, we investigate in detail different grammars, the use of morphological knowledge from outside sources, and the use …...

AbstractComputational morphological segmentation has been an active research topic for decades as it is beneficial for many natural language processing tasks. With the high cost of manually labeling data for …...