Having mentioned some of the traditional classification methods, we now sketch two techniques that have become particularly prominent in statistical NLP since the 1990s. The first, with mathematical roots dating to the 1950s, is maximum entropy (MaxEnt), also called (multinomial) logistic regression (e.g., Ratnaparkhi 1997). Features in this case are any desired 0, 1-valued (binary) functions of both a given linguistic input and a possible class. (For continuous features, supervised or unsupervised discretization methods may be applied, such as entropy-based partitioning into some number of intervals.) Training data provide occurrence frequencies for these features, and a distribution is derived for the conditional probability of a class, given a linguistic input. (As such, it is a discriminative method.) As its name implies, this conditional probability function is a maximum-entropy distribution, constrained to conform with the binary feature frequencies observed in the training data. Its form (apart from a constant multiplier) is an exponential whose exponent is a linear combination of the binary feature values for a given input and given class. Thus it is a log-linear model (a distribution whose logarithm is linear in the features)—a type of model now prevalent in many statistical NLP tasks.  Note that since its logarithm is a linear combination of binary feature values for any given input and any given class, choosing the maximum-probability class for a given input amounts to linear decision-making, much as in some of the classical methods; however, MaxEnt generally provides better classification performance, and the classification probabilities it supplies can be useful in further computations (e.g., expected utilities).