1. A method for sequence tagging medical patient records comprising the steps of: providing a labeled corpus D={X l ,Y l } l= M of M sentences taken from a set of medical records, wherein X=x i. .. x n is a sentence, x j is a word at position j from a finite vocabulary V X , each word x j having a label y j from a finite vocabulary V Y , and Y is a collection of labels for the sentence X, wherein said labels are sequence tags for sentence X; initializing generative parameters θ and discriminative parameters {tilde over (θ)}, wherein θ={p(x i |y j ), p(y i |y j ), ∀y i , y j εV Y , ∀x i εV X } are generative parameters and {tilde over (θ)}={{tilde over (p)}(x i |y j ), {tilde over (p)}(y i |y j ), ∀y i , y j ε=V Y , ∀x i εV X } are discriminative parameters, and p(x i |y j ), {tilde over (p)}(x i |y j ) are emission probabilities and p(y i |y j ), {tilde over (p)}(y i |y j ) are transition probabilities; and finding parameters θ,{tilde over (θ)} that maximize a functional LL−C×Penalty, wherein LL is a log-likelihood function LL = log p ( θ , θ ~ ) + ∏ l = 1 M [ log p ( X l , Y l  θ ~ ) - log p ( X l  θ ~ ) ] + ∏ l = 1 M log p ( X l  θ ) , wherein p ( θ , θ ~ ) ∝ p ( θ ) p ( θ ~ ) 1 σ exp ( -  θ - θ ~  2 2 σ 2 ) is a prior wherein σ is a tradeoff parameter that balances contributions of the generative parameters and discriminative parameters, p ( X l , Y l  θ ~ ) = ∏ t = 1 n p ( x t l  y t l , θ ~ ) p ( y t l  y t - 1 l , θ ~ ) , and p(X L |{tilde over (θ)}), p(X l |θ) are calculated as p ( X  θ ) = ∑ y α t ( y  θ ) β t ( y  θ ) , wherein α t ( y t ) = p ( x 1 , … , x t , y t ) = p ( x t | y t ) ∑ y p ( y t | y ) α t - 1 ( y ) , with α 0 =1, and β t ( y t ) = p ( x t + 1 , … , x n | y t ) = ∑ y β t + 1 ( y ) p ( x t + 1 | y ) p ( y | y t ) with β t =1, Penalty is a penalty function defined as ∑ y ∈ V Y ( em y 2 + tr y 2 + e m ~ y 2 + t r ~ y 2 ) , where em y =1−Σ ∀x i εV X p(x i |y), e{tilde over (m)} y =1−Σ ∀x i εV X {tilde over (p)}(x i |y) are emission probability constraints for state y and tr y =1−Σ ∀y i εV Y p(y i |y), t{tilde over (r)} y =1−Σ ∀y i εV Y {tilde over (p)}(y i |y) are transition probability constraints for state, and C is a constant, wherein parameters θ,{tilde over (θ)} are the probabilities that a new sentence X′ is labeled as Y′.