Timezone: »

Conditional Random Fields with High-Order Features for Sequence Labeling
Nan Ye · Wee Sun Lee · Hai Leong Chieu · Dan Wu

Tue Dec 08 07:00 PM -- 11:59 PM (PST) @ None #None

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 in the features used 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.

Author Information

Nan Ye (National University of Singapore)
Wee Sun Lee (National University of Singapore)
Hai Leong Chieu (DSO National Laboratories)
Dan Wu

More from the Same Authors