Generalized isotonic conditional random fields

被引：5

作者：

Mao, Yi ^{[1
]}

Lebanon, Guy ^{[1
]}

机构：

[1] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA

来源：

MACHINE LEARNING | 2009年 / 77卷 / 2-3期

关键词：

Conditional random fields; Isotonic constraints; Prior elicitation; Sentiment prediction; Information extraction;

D O I：

10.1007/s10994-009-5139-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Conditional random fields are one of the most popular structured prediction models. Nevertheless, the problem of incorporating domain knowledge into the model is poorly understood and remains an open issue. We explore a new approach for incorporating a particular form of domain knowledge through generalized isotonic constraints on the model parameters. The resulting approach has a clear probabilistic interpretation and efficient training procedures. We demonstrate the applicability of our framework with an experimental study on sentiment prediction and information extraction tasks.

引用

页码：225 / 248

页数：24

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