Randomized rule selection in transformation-based learning: a comparative study

摘要

Transformation-Based Learning (TBL) is a relatively new machine learning method that has achieved notable success on language problems. This paper presents a variant of TBL, called Randomized TBL, that overcomes the training time problems of standard TBL without Sacrificing accuracy. It include a set of experiments on part-of-speech tagging in which the Size of the corpus and template set are varied. The results show that Randomize TBL Can address problems that are intractable in terms of training time for standard TBL.