Structure learning for natural langauge processing

Y Ni; C Saunders; S Szedmak; M Niranjan

Structure learning for natural langauge processing

Y Ni, C Saunders, S Szedmak, M Niranjan

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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 andmachine translation (MT), illustrating the effectiveness of the model.

Translated title of the contribution	Structure learning for natural langauge processing
Original language	English
Title of host publication	the 11th IEEE International Workshop on Machine Learning Signal Processing, France
Publication status	Published - 2009

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Conference Organiser: MLSP

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@inproceedings{5ea1f8d3774842b89d31dd1b1a182581,

title = "Structure learning for natural langauge processing",

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 andmachine translation (MT), illustrating the effectiveness of the model.",

author = "Y Ni and C Saunders and S Szedmak and M Niranjan",

note = "Conference Organiser: MLSP",

year = "2009",

language = "English",

booktitle = "the 11th IEEE International Workshop on Machine Learning Signal Processing, France",

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TY - GEN

T1 - Structure learning for natural langauge processing

AU - Ni, Y

AU - Saunders, C

AU - Szedmak, S

AU - Niranjan, M

N1 - Conference Organiser: MLSP

PY - 2009

Y1 - 2009

N2 - 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 andmachine translation (MT), illustrating the effectiveness of the model.

AB - 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 andmachine translation (MT), illustrating the effectiveness of the model.

M3 - Conference Contribution (Conference Proceeding)

BT - the 11th IEEE International Workshop on Machine Learning Signal Processing, France

ER -

Structure learning for natural langauge processing

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