Sample Selection for Statistical Parsing

Sample Selection for Statistical Parsing

Corpus-based statistical parsing relies on using large quantities of annotated text as training examples. Building this kind of resource is expensive and labor-intensive. This work proposes to use sample selection to find helpful training examples and reduce human effort spent on annotating less informative ones. We consider several criteria for predicting whether unlabeled data might be a help...

Discriminative Sample Selection for Statistical Machine Translation

Production of parallel training corpora for the development of statistical machine translation (SMT) systems for resource-poor languages usually requires extensive manual effort. Active sample selection aims to reduce the labor, time, and expense incurred in producing such resources, attaining a given performance benchmark with the smallest possible training corpus by choosing informative, nonr...

Sample Selection for Statistical Grammar Induction

Corpus-based grz.mmar induction relies on using many hand-parsed sentences as training examples. However, the construction of a training corpus with detailed syntactic analysis for every sentence is a labor-intensive task. We propose to use sample selection methods to minimize the amount of annotation needed in the training data, thereby reducing the workload of the human annotators. This paper...

Model Selection for Mixture Models Using Perfect Sample

We have considered a perfect sample method for model selection of finite mixture models with either known (fixed) or unknown number of components which can be applied in the most general setting with assumptions on the relation between the rival models and the true distribution. It is, both, one or neither to be well-specified or mis-specified, they may be nested or non-nested. We consider mixt...

Statistical Parsing