CRF Recitation Kevin Tang. Conditional Random Field Definition.

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Presentation transcript:

CRF Recitation Kevin Tang

Conditional Random Field Definition

Meaning of Graphical Model

Discriminative v.s. Generative Y=0Y=1 X=1 1/20 X=2 1/4 Y=0Y=1 X=1 10 X=2 1/2 Stolen from: Also, see bayes.pdfhttp://papers.nips.cc/paper/2020-on-discriminative-vs-generative-classifiers-a-comparison-of-logistic-regression-and-naive- bayes.pdf

Comparison To HMMs  Audience thoughts?

Comparison To HMMs  Similarities:  Both probabilistic models  Both use the Markov Property as an assumption  Differences  CRFs are discriminative while HMM’s are generative  CRFs may have more accuracy with sequence tagging as it directly models p(y|x)  HMMs use Bayes Rule to model tagging  HMMs can generate samples from the distribution p(x, y) and are often more robust (missing labels, unsupervised, or semisupervised)  Hmms can handle missing labels

Let’s summarize terminology and symbols

Other Formulae/Symbols we may see

Objective of Gradient Descent

Nesterov’s accelerated gradient descent

Summary of Gradient Descent  Pregenerate phis  Calculate dF  Calculate dlogZ  Generate Gs, generate alphas, betas  Run forward backwards algorithm with normalization  Calculate dw = dF – dlogZ  Update w = w + dw or use Nesterov  End after number of iterations, or when change hits a minimum, or percent change hits a minimum.

Some numbers for sanity purposes  Stuff that I got  ~250 iterations with Nesterov acceleration (will vary depending on your growth factor)  ~5 minutes computational time in Matlab Much faster when outside of a Matlab Class…(more like 1 minute)  ~30 minutes on a very unoptimized solution (but hey, it worked)  Could get faster with more vectorization, but I’m lazy.  You probably will have better luck in Python (grumble grumble)  ~50% hamming loss