[1] Similar to commonly used supervised learning techniques, structured prediction models are typically trained by means of observed data in which the predicted value is compared to the ground truth, and this is used to adjust the model parameters.
An example application is the problem of translating a natural language sentence into a syntactic representation such as a parse tree.
Structured prediction is used in a wide variety of domains including bioinformatics, natural language processing (NLP), speech recognition, and computer vision.
Sequence tagging is a class of problems prevalent in NLP in which input data are often sequential, for instance sentences of text.
is done using an algorithm such as Viterbi or a max-sum, rather than an exhaustive search through an exponentially large set of candidates.