Structure learning for natural language processing
Yizhao Ni, Craig Saunders, Sandor Szedmak and Mahesan Niranjan
In: the 11th IEEE International Workshop on Machine Learning for Signal Processing, Grenoble, France(2009).
We applied a structure learning model, Max-Margin Structure (MMS), to natural language processing (NLP) tasks, where the aim is to capture the latent relationships within the output language domain. We formulate this model as an extension of multi-class Support Vector Machine (SVM) and present a perceptron-based learning approach to solve the problem. Experiments are carried out on two related NLP tasks: part-of-speech (POS) tagging and machine translation (MT), illustrating the effectiveness of the model.