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• W2117667029 abstract "Dependencies among neighbouring labels in a sequence is an important source of information for sequence labeling problems. However, only dependencies between adjacent labels are commonly exploited in practice because of the high computational complexity of typical inference algorithms when longer distance dependencies are taken into account. In this paper, we show that it is possible to design efficient inference algorithms for a conditional random field using features that depend on long consecutive label sequences (high-order features), as long as the number of distinct label sequences used in the features is small. This leads to efficient learning algorithms for these conditional random fields. We show experimentally that exploiting dependencies using high-order features can lead to substantial performance improvements for some problems and discuss conditions under which high-order features can be effective." @default.
• W2117667029 created "2016-06-24" @default.
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• W2117667029 date "2009-12-07" @default.
• W2117667029 modified "2023-09-24" @default.
• W2117667029 title "Conditional Random Fields with High-Order Features for Sequence Labeling" @default.
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