Transformation-based learning (TBL) is a machine learning method for, in particular, sequential classification, invented by Eric Brill [Brill 1993b[Brill , 1995a. It is widely used within computational linguistics and natural language processing, but surprisingly little in other areas.TBL is a simple yet flexible paradigm, which achieves competitive or even state-of-the-art performance in several areas and does not overtrain easily. It is especially successful at catching local, fixed-distance dependencies and seamlessly exploits information from heterogeneous discrete feature types. The learned representation-an ordered list of transformation rules-is compact and efficient, with clear semantics. Individual rules are interpretable and often meaningful to humans.The present article offers a survey of the most important theoretical work on TBL, addressing a perceived gap in the literature. Because the method should be useful also outside the world of computational linguistics and natural language processing, a chief aim is to provide an informal but relatively comprehensive introduction, readable also by people coming from other specialities. ACM Reference Format:Marcus Uneson. 2014. When errors become the rule: Twenty years with transformation-based learning.
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