Scaling up context-sensitive text correction

The main challenge in an effort to build a realistic system with context-sensitive inference capabilities, beyond accuracy, is scalability. This paper studies this problem in the context of a learning-based approach to context sensitive text correction - the task of fixing spelling errors that result in valid words, such as substituting to for too, casual for causal, and so on. Research papers on this problem have developed algorithms that can achieve fairly high accuracy, in many cases over 90%. However, this level of performance is not sufficient for a large coverage practical system since it implies a low sentence level performance. We examine and offer solutions to several issues relating to scaling up a context sensitive text correction system. In particular, we suggest methods to reduce the memory requirements while maintaining a high level of performance and show that this can still allow the system to adapt to new domains. Most important, we show how to significantly increase the coverage of the system to realistic levels, while providing a very high level of performance, at the 99% level.