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 | |
| 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 | China |
| City | HangZhou |
| Period | 8/11/12 → 8/13/12 |
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ASJC Scopus subject areas
- Software
Cite this
Parameter estimation of conditional Random fields model based on cloud computing. / Chen, Wenguang; Li, Yangyang; Wang, Haoyi; Chiang, I. Jen.
Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012. 2012. p. 59-62 6468560.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
ER -