Conditional random field

Class of statistical modeling methods In pattern recognition and machine learning, conditional random field (CRF) is a class of statistical modeling methods often used for structured prediction. Unlike a classifier which predicts a label for a single sample without considering neighboring samples, a CRF can take context into account. To do so, the predictions are modeled as a graphical model, which represents the presence of dependencies between the predictions. The kind of graph used depends on the application. For example, in natural language processing, linear chain CRFs are popular, for which each prediction is dependent only on its immediate neighbors. In image processing, the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions. Other examples where CRFs are used are: labeling or parsing of sequential data for natural language processing or biological sequences, part-of-speech tagging, shallow parsing, named entity recognition, gene finding, peptide critical functional region finding, and object recognition and image segmentation in computer vision. Description CRFs are a type of discriminative undirected proba