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An Introduction to Conditional Random Fields

Foundations and Trends R. in Machine Learning Vol. 4, No. 4 (2011) 267 373.. c 2012 C. Sutton and A. McCallum DOI: An Introduction to Conditional Random Fields By Charles Sutton and Andrew McCallum Contents 1 Introduction 268. Implementation Details 271. 2 Modeling 272. Graphical Modeling 272. Generative versus Discriminative Models 278. Linear-chain CRFs 286. General CRFs 290. Feature Engineering 293. Examples 298. Applications of CRFs 306. Notes on Terminology 308. 3 Overview of Algorithms 310. 4 Inference 313. Linear-Chain CRFs 314. Inference in Graphical Models 318. Implementation Concerns 328. 5 Parameter Estimation 331. Maximum Likelihood 332. stochastic Gradient Methods 341. Parallelism 343. Approximate Training 343. Implementation Concerns 350. 6 Related Work and Future Directions 352. Related Work 352. frontier Areas 359.

5.2 Stochastic Gradient Methods 341 5.3 Parallelism 343 5.4 Approximate Training 343 5.5 Implementation Concerns 350 6 Related Work and Future Directions 352 6.1 Related Work 352 6.2 Frontier Areas 359 Acknowledgments 362 References 363

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