Conditional Random Fields (CRFs) have been applied with considerable success to a number of natural language processing tasks. However, these tasks have mostly involved very small label sets. When deployed on tasks with larger label sets, the requirements for computational resources mean that training becomes intractable. This paper describes a method for training CRFs on such tasks, using error correcting output codes (ECOC). A number of CRFs are independently trained on the separate binary

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... elling tasks of distinguishing between a subset of the labels and its complement. During decoding, these models are combined to produce a predicted label sequence which is resilient to errors by individual models. Error-correcting CRF training is much less resource intensive and has a much faster training time than a standardly formulated CRF, while decoding performance remains quite comparable. This allows us to scale CRFs to previously impossible tasks, as demonstrated by our experiments with large label sets.