The application of structured learning in natural language processing

We propose a structured learning approach,max-margin structure (MMS), which is targeted at natural language processing (NLP) tasks. The architecture of our approach is shown to capture structural aspects of the problem domains, leading to demonstrable performance improvements on two NLP tasks: part-of-speech tagging and statistical machine translation (SMT).We present a perceptron-based online learning algorithmto train themodel and demonstrate desirable computational scaling behavior over traditional optimisation methods.