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Structured prediction is a particular discipline applied to machine learning in which machine learning techniques predict structured objects. Typically, the structured prediction makes use of supervised machine learning programs with labels that can apply in order to produce outcomes.
Techopedia Explains Structured Prediction
One of the simplest and easiest ways to talk about structured prediction is that it uses training problems to solve a classification task. A resource available from NeurIPS quoted by Sasha Rush in July of 2010 describes it as: “a framework for solving problems of classification or regression in which the output variables are mutually dependent or constrained.”
Specifically, when a prediction cannot be solved by direct observation of all possible values, the structured prediction takes inputs, and uses them to predict the results.
Alexander Passos, then a PhD ML student at UNICAMP in Brazil, gives an interesting definition of structure prediction in Quora that is abundantly useful in characterizing this sort of utility: “Structured prediction is a special case of multi class classification (that is, given x predict y) where:
- There are far too many possible values for y (exponential or infinite).
- However, these values are not opaque, and inspecting their structure can help you design a classifier that learns from few examples (in relation to the cardinality of y) in a short amount of time.”
Structured prediction has been useful in natural language processing, bioscience research and other disciplines. For instance, using sequence tagging and parse trees, a structure prediction program can achieve various natural language processing goals.
