Conditional Random Fields (CRF) - Explained
This video explains Conditional Random Fields (CRFs) by building directly on Hidden Markov Models (HMMs), showing how CRFs move from generative modeling to discriminative sequence labeling. It covers linear chain CRFs, feature functions, conditional probability, normalization with the partition function, and why CRFs are widely used for structured prediction tasks like part-of-speech tagging and named entity recognition in machine learning and natural language processing.
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Contents
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00:00 - Intro
00:11 - HMMs recap
00:47 - HMMs limitations
01:04 - CRFs
01:44 - Feature Functions
02:36 - Outro
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