CRF-based Learning Algorithm

A CRF-based Learning Algorithm is a Maximum Entropy-based Learning Algorithm that trains a Conditional Random Field Model.

• AKA: CRF Training Algorithm, Conditional Random Field Learning Algorithm, CRF Algorithm, Conditional Random Field Modeling Algorithm.
• Context:
  • It can require a Parameter Estimation Algorithm.
    • which can have a high Computational Cost.
• See: HMM-based Learning Algorithm, MEMM-based Learning Algorithm, Logistic Regression Algorithm.

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References

2009

• (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Conditional_random_field
  • A conditional random field (CRF) is a type of discriminative probabilistic model most often used for the labeling or parsing of sequential data, such as natural language text or biological sequences.

2003

• (McCallum & Li, 2003) ⇒ Andrew McCallum, and Wei Li. (2003). “Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons.” In: Proceedings of Seventh Conference on Natural Language Learning (CoNLL 2003).

2001

• (LMP, 2001) ⇒ John D. Lafferty, Andrew McCallum, and Fernando Pereira. (2001). “Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.] Proceedings of ICML 2001.
  • "Let G = (V,E) be a graph such that Y = (Yv)v2V, so that Y is indexed by the vertices

of G. Then (X,Y) is a conditional random field in case, when conditioned on X, the random variables Yv obey the Markov property with respect to the graph: p(Yv |X,Yw,w 6= v) = p(Yv |X,Yw,w v), where [math]\displaystyle{ w }[/math] v means that [math]\displaystyle{ w }[/math] and v are neighbors in G."

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