Structure learning for natural language processing
Structure learning for natural language processing
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 VectorMachine (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.
Ni, Yizhao
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Saunders, Craig
26634635-4d4d-4469-b9ec-1d68788aa47a
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
2009
Ni, Yizhao
0452e056-90d0-4feb-a97b-ff2689b6b492
Saunders, Craig
26634635-4d4d-4469-b9ec-1d68788aa47a
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Ni, Yizhao, Saunders, Craig, Szedmak, Sandor and Niranjan, Mahesan
(2009)
Structure learning for natural language processing.
In 2009 IEEE International Workshop on Machine Learning for Signal Processing.
IEEE.
6 pp
.
(doi:10.1109/MLSP.2009.5306193).
Record type:
Conference or Workshop Item
(Paper)
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 VectorMachine (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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structure_learning_for_NLP
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Published date: 2009
Venue - Dates:
IEEE Workshops on Machine Learning for Signal Processing, 2009, , Grenoble, France, 2009-09-02 - 2009-09-04
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 270324
URI: http://eprints.soton.ac.uk/id/eprint/270324
ISSN: 1551-2541
PURE UUID: cfa1d287-d418-4b96-8bed-819c86b95238
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Date deposited: 21 Apr 2010 07:46
Last modified: 16 Mar 2024 03:55
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Contributors
Author:
Yizhao Ni
Author:
Craig Saunders
Author:
Sandor Szedmak
Author:
Mahesan Niranjan
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