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2009

Efficient Inference of CRFs for Large-Scale Natural Language Data

10 years 9 months ago
Efficient Inference of CRFs for Large-Scale Natural Language Data
This paper presents an efficient inference algorithm of conditional random fields (CRFs) for large-scale data. Our key idea is to decompose the output label state into an active set and an inactive set in which most unsupported transitions become a constant. Our method unifies two previous methods for efficient inference of CRFs, and also derives a simple but robust special case that performs faster than exact inference when the active sets are sufficiently small. We demonstrate that our method achieves dramatic speedup on six standard natural language processing problems.
Minwoo Jeong, Chin-Yew Lin, Gary Geunbae Lee
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Minwoo Jeong, Chin-Yew Lin, Gary Geunbae Lee
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