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STRUCTURE LEARNING FOR NATURAL LANGUAGE PROCESSING

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
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
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 At IEEE Workshops on Machine Learning for Signal Processing, 2009, France. 02 - 04 Sep 2009.

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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More information

Published date: 2009
Additional Information: Event Dates: Sep 2, 2009 - Sep 4, 2009
Venue - Dates: IEEE Workshops on Machine Learning for Signal Processing, 2009, France, 2009-09-02 - 2009-09-04
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 270915
URI: http://eprints.soton.ac.uk/id/eprint/270915
PURE UUID: 8a63cc73-379a-4ca9-a9b4-9f9fcdaa41b3

Catalogue record

Date deposited: 23 Apr 2010 10:51
Last modified: 18 Jul 2017 06:49

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Contributors

Author: Yizhao Ni
Author: Craig Saunders
Author: Sandor Szedmak

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