This paper presents a way of utilizing statistical decision trees to systematically raise the memory capacity of Markov models and effectively to make Markov models be able to accommodate various features.
for example, we often find that in some cases a certain lexical context can improve the performance of an HMM-based POS tagger, but incorporating such additional features is not easy and it may even degrade the overall performance.