PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:5663
Deposited By:Yizhao Ni
Deposited On:08 March 2010