Abstraction is harmful in language learning
Walter M. P. Daelemans · 1998
The usual approach to learning language processing tasks such as tagging, parsing, grapheme-to-phoneme conversion, pp-attachment, etc., is to extract regularities from training data in the form of decision trees, rules, probabilities or other abstractions. These representations of regularities are then used to solve new cases of the task. The individual training examples on which the abstractions were based are discarded (forgotten). While this approach seems to work well for other application areas of Machine Learning, I will show that there is evidence that it is not the best way to learn language processing tasks.