How Transformation based learning works?
Transformation based learning usually starts with some simple solution to the problem. Then it runs through cycles. At each cycle, the transformation which gives more benefit is chosen and applied to the problem. The algorithm stops when the selected transformations do not add more value or there are no more transforamtions to be selected. This is like painting a wall with background color first, then paint different color in each block as per its shape or so.TBL is best suitable for classification tasks. In TBL, accuracy is generally considered as the objective function. So in each training cycle, the tagger finds the transformations that greatly reduce the errors in the training set. This transormation is then added to the transformation list and applied to the training corpus. At the end of the training, the tagger is run by first tagging the fresh text with initial-state annotator, then applying each transformation in order wherever it can apply. Advantages of Transformation Based
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