Parameter estimation of conditional Random fields model based on cloud computing

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

研究成果: 書貢獻/報告類型會議貢獻

摘要

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.

原文英語
主出版物標題Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
頁面59-62
頁數4
DOIs
出版狀態已發佈 - 2012
事件2012 IEEE International Conference on Granular Computing, GrC 2012 - HangZhou, 中国
持續時間: 8月 11 20128月 13 2012

出版系列

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

其他

其他2012 IEEE International Conference on Granular Computing, GrC 2012
國家/地區中国
城市HangZhou
期間8/11/128/13/12

ASJC Scopus subject areas

  • 軟體

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