Parameter estimation of conditional Random fields model based on cloud computing

Wenguang Chen; Yangyang Li; Haoyi Wang; I. Jen Chiang

doi:10.1109/GrC.2012.6468560

Parameter estimation of conditional Random fields model based on cloud computing

Wenguang Chen, Yangyang Li, Haoyi Wang, I. Jen Chiang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Conditional Random Field (CRF), a type of conditional probability model, has been widely used in Nature Language Processing (NLP), such as sequential data segmentation and labeling. The advantage of CRF model is the ability to express long-distance-dependent and overlapping features. However, the model parameter estimation of CRF is very time-consuming because of the large amount of calculation. This paper describes the method that use of MapReduce model to parallel estimate the model parameters of CRF in open-source and distributed computing framework that provided by Hadoop. Experiments demonstrated that the proposed method can effectively reduce the time complexity of model parameter estimation.

Original language	English
Title of host publication	Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
Pages	59-62
Number of pages	4
DOIs	https://doi.org/10.1109/GrC.2012.6468560
Publication status	Published - 2012
Externally published	Yes
Event	2012 IEEE International Conference on Granular Computing, GrC 2012 - HangZhou, China Duration: Aug 11 2012 → Aug 13 2012

Other

Other	2012 IEEE International Conference on Granular Computing, GrC 2012
Country/Territory	China
City	HangZhou
Period	8/11/12 → 8/13/12

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10.1109/GrC.2012.6468560

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@inproceedings{112aacd1be7a4d1291f9e7e5a9500666,

title = "Parameter estimation of conditional Random fields model based on cloud computing",

abstract = "Conditional Random Field (CRF), a type of conditional probability model, has been widely used in Nature Language Processing (NLP), such as sequential data segmentation and labeling. The advantage of CRF model is the ability to express long-distance-dependent and overlapping features. However, the model parameter estimation of CRF is very time-consuming because of the large amount of calculation. This paper describes the method that use of MapReduce model to parallel estimate the model parameters of CRF in open-source and distributed computing framework that provided by Hadoop. Experiments demonstrated that the proposed method can effectively reduce the time complexity of model parameter estimation.",

author = "Wenguang Chen and Yangyang Li and Haoyi Wang and Chiang, {I. Jen}",

year = "2012",

doi = "10.1109/GrC.2012.6468560",

language = "English",

isbn = "9781467323093",

pages = "59--62",

booktitle = "Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012",

note = "2012 IEEE International Conference on Granular Computing, GrC 2012 ; Conference date: 11-08-2012 Through 13-08-2012",

}

TY - GEN

T1 - Parameter estimation of conditional Random fields model based on cloud computing

AU - Chen, Wenguang

AU - Li, Yangyang

AU - Wang, Haoyi

AU - Chiang, I. Jen

PY - 2012

Y1 - 2012

N2 - Conditional Random Field (CRF), a type of conditional probability model, has been widely used in Nature Language Processing (NLP), such as sequential data segmentation and labeling. The advantage of CRF model is the ability to express long-distance-dependent and overlapping features. However, the model parameter estimation of CRF is very time-consuming because of the large amount of calculation. This paper describes the method that use of MapReduce model to parallel estimate the model parameters of CRF in open-source and distributed computing framework that provided by Hadoop. Experiments demonstrated that the proposed method can effectively reduce the time complexity of model parameter estimation.

AB - Conditional Random Field (CRF), a type of conditional probability model, has been widely used in Nature Language Processing (NLP), such as sequential data segmentation and labeling. The advantage of CRF model is the ability to express long-distance-dependent and overlapping features. However, the model parameter estimation of CRF is very time-consuming because of the large amount of calculation. This paper describes the method that use of MapReduce model to parallel estimate the model parameters of CRF in open-source and distributed computing framework that provided by Hadoop. Experiments demonstrated that the proposed method can effectively reduce the time complexity of model parameter estimation.

UR - http://www.scopus.com/inward/record.url?scp=84875080144&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84875080144&partnerID=8YFLogxK

U2 - 10.1109/GrC.2012.6468560

DO - 10.1109/GrC.2012.6468560

M3 - Conference contribution

AN - SCOPUS:84875080144

SN - 9781467323093

SP - 59

EP - 62

BT - Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012

T2 - 2012 IEEE International Conference on Granular Computing, GrC 2012

Y2 - 11 August 2012 through 13 August 2012

ER -

Parameter estimation of conditional Random fields model based on cloud computing

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