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Semi-Markov Conditional Random Field with High-Order Features Viet Cuong Nguyen

By Nan Ye, Wee Sun Lee and Hai Leong Chieu

Abstract

We extend first-order semi-Markov conditional random fields (semi-CRFs) to include higherorder semi-Markov features, and present efficient inference and learning algorithms, under the assumption that the higher-order semi-Markov features are sparse. We empirically demonstrate that high-order semi-CRFs outperform high-order CRFs and first-order semi-CRFs on three sequence labeling tasks with long distance dependencies. 1

Year: 2012
OAI identifier: oai:CiteSeerX.psu:10.1.1.228.7364
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