Conditional Random Fields with High-Order Features for Sequence Labeling
Conditional Random Fields with High-Order Features for Sequence Labeling
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发表时间：
2009-12
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通讯作者：
N. Ye;Wee Sun Lee;Hai Leong Chieu;Dan Wu
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作者：
N. Ye;Wee Sun Lee;Hai Leong Chieu;Dan Wu
序列中相邻标签之间的依赖性是序列标记问题的重要信息源。然而，在实践中通常只利用相邻标签之间的依赖关系，因为当考虑较长距离的依赖关系时，典型推理算法的计算复杂度很高。在本文中，我们表明，只要特征中使用的不同标签序列的数量很少，就可以使用依赖于长连续标签序列（高阶特征）的特征为条件随机场设计有效的推理算法。这导致了这些条件随机场的有效学习算法。我们通过实验证明，使用高阶特征利用依赖关系可以显着提高某些问题的性能，并讨论高阶特征有效的条件。
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.