Conditional random \ufb01elds 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 ef\ufb01cient training procedures. We demonstrate the applicability of our framework with an experimental study on sentiment prediction and information extraction tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"
Conditional random \ufb01elds 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. 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