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 proceedingConference 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 languageEnglish
Title of host publicationProceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
Pages59-62
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE International Conference on Granular Computing, GrC 2012 - HangZhou, China
Duration: Aug 11 2012Aug 13 2012

Other

Other2012 IEEE International Conference on Granular Computing, GrC 2012
CountryChina
CityHangZhou
Period8/11/128/13/12

ASJC Scopus subject areas

  • Software

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  • Cite this

    Chen, W., Li, Y., Wang, H., & Chiang, I. J. (2012). Parameter estimation of conditional Random fields model based on cloud computing. In Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012 (pp. 59-62). [6468560] https://doi.org/10.1109/GrC.2012.6468560