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Structure learning for natural language processing

Saunders, C.J., Szedmak, S. and Niranjan, M. (2009) Structure learning for natural language processing Proceedings of the 2009 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009), 6 pp.-.

Record type: Article


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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Published date: 2009
Additional Information: Imported from ISI Web of Science
Organisations: Southampton Wireless Group


Local EPrints ID: 270324
PURE UUID: cfa1d287-d418-4b96-8bed-819c86b95238

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Date deposited: 21 Apr 2010 07:46
Last modified: 18 Jul 2017 06:51

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Author: C.J. Saunders
Author: S. Szedmak
Author: M. Niranjan

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