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Scaling conditional random fields

Xem 1-3 trên 3 kết quả Scaling conditional random fields
  • Conditional Random Fields (CRFs) have been applied with considerable success to a number of natural language processing tasks. However, these tasks have mostly involved very small label sets. When deployed on tasks with larger label sets, the requirements for computational resources mean that training becomes intractable. This paper describes a method for training CRFs on such tasks, using error correcting output codes (ECOC). A number of CRFs are independently trained on the separate binary labelling tasks of distinguishing between a subset of the labels and its complement. ...

    pdf8p bunbo_1 17-04-2013 32 3   Download

  • Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynamic programming parsers have restricted training and evaluation to artificially short sentences, we present the first general, featurerich discriminative parser, based on a conditional random field model, which has been successfully scaled to the full WSJ parsing data.

    pdf9p hongphan_1 15-04-2013 31 1   Download

  • Conditional Random Fields (CRFs) are a widely-used approach for supervised sequence labelling, notably due to their ability to handle large description spaces and to integrate structural dependency between labels. Even for the simple linearchain model, taking structure into account implies a number of parameters and a computational effort that grows quadratically with the cardinality of the label set. In this paper, we address the issue of training very large CRFs, containing up to hundreds output labels and several billion features.

    pdf10p hongdo_1 12-04-2013 31 1   Download

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