Memory Based Learning in NLP
We study memory-based learning methods and show that they can be viewed as learning linear predictors over commonly used feature spaces. We suggest that this view allows one to study memory based methods and other successful learning algorithms used in NLP within the same computational framework and may result in improved algorithms and a better understanding for the role of learning in natural language inferences.