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Conditional Random Fields (CRF) CRF is a discriminant model for sequences data similar to MEMM. It models the dependency between each state and the entire input sequences. Unlike MEMM, CRF overcomes the label bias issue by using global normalizer.

Gaussian conditional random fields (GCRF) are a well-known used structured model for con- tinuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits dependence structure among outputs, which is provided by a similarity measure.

CRF is an undirected graph-based model that considered words that not only occur before the entity but also after it. The training data can be annotated by using GATE architecture. The Python code provided helps in training a CRF model and extracting entities from text.

To train a CRF model, we need to create features for each of the s in the sentences. One particularly useful feature in NLP is the part-of-speech (POS) tags of the words. They indicates whether a word is a noun, a verb or an adjective. (In fact, a POS tagger is also usually a trained CRF model.)

Case report form (CRF) is a specialized document in clinical research. It should be study protocol driven, robust in content and have material to collect the study specific data.

In NLP, CRFs are used for the sequence labeling tasks, which involve assigning labels to each element in a sequence of observations, such as assigning part-of-speech tags to words in a sentence or recognizing named entities (such as people, organizations, and locations) in a text.

Conditional Random Fields (CRFs) are often used to improve the output of an initial segmentation model, such as a convolutional neural network (CNN). Conventional CRF approaches in medical imaging use manually defined features, such as intensity to improve appearance similarity or location to improve spatial coherence.

Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account.