Sciweavers

Share
ICCPOL
2009
Springer

A Simple and Efficient Model Pruning Method for Conditional Random Fields

9 years 3 months ago
A Simple and Efficient Model Pruning Method for Conditional Random Fields
Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational machines, trained CRFs Models for a real application quickly inflate. Recently, researchers often have to use models with tens of millions features. This paper considers pruning an existing CRFs model for storage reduction and decoding speedup. We propose a simple but efficient rank metric for feature group rather than features that previous work usually focus on. A series of experiments in two typical labeling tasks, word segmentation and named entity recognition for Chinese, are carried out to check the effectiveness of the proposed method. The results are quite positive and show that CRFs models are highly redundant, even using carefully selected label set and feature templates. Key words: Conditional Random Fields, Model Pruning
Hai Zhao, Chunyu Kit
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICCPOL
Authors Hai Zhao, Chunyu Kit
Comments (0)
books