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Natural Language Processing with Java and LingPipe Cookbook by Krishna Dayanidhi, Breck Baldwin

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CRFs for chunking

CRFs are best known to provide close to state-of-the-art performance for named-entity tagging. This recipe will tell us how to build one of these systems. The recipe assumes that you have read, understood, and played with the Conditional r andom fields – CRF for word/token tagging recipe in Chapter 4, Tagging Words and Tokens, which addresses the underlying technology. Like HMMs, CRFs treat named entity detection as a word-tagging problem, with an interpretation layer that provides chunkings. Unlike HMMs, CRFs use a logistic-regression-based classification approach, which, in turn, allows for random features to be included. Also, there is an excellent tutorial on CRFs that this recipe follows closely (but omits details) at

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