Title :
Structure learning for natural language processing
Author :
Ni, Yizhao ; Saunders, Craig J. ; Szedmak, Sandor ; Niranjan, Mahesan
Author_Institution :
Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK
Abstract :
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.
Keywords :
learning (artificial intelligence); natural language processing; support vector machines; Max-Margin Structure model; machine translation task; natural language processing; part-of-speech tagging task; perceptron-based learning approach; structure learning model; support vector machine; Computer science; Entropy; Hidden Markov models; Intersymbol interference; Machine learning; Maximum likelihood estimation; Natural language processing; Support vector machines; Surface-mount technology; Tagging;
Conference_Titel :
Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on
Conference_Location :
Grenoble
Print_ISBN :
978-1-4244-4947-7
Electronic_ISBN :
978-1-4244-4948-4
DOI :
10.1109/MLSP.2009.5306193
