Conditional Random Fields (CRFs) are a type of statistical modeling method used for structured prediction, particularly in the context of natural language processing. They are particularly useful for tasks that involve labeling and segmenting sequences, where the prediction of one label depends on the context provided by surrounding labels. CRFs allow for a flexible framework that can incorporate a variety of features, making them effective for modeling complex relationships in sequential data.
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