1. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform a method for sequence tagging medical patient records, the method comprising the steps of: providing a labeled corpus D={X l ,Y l } l=1 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 εV 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 - ∑ ∀ x i ∈ V X ⁢ ⁢ p ⁡ ( x i ❘ y ) , e ⁢ m ~ y = 1 - ∑ ∀ x i ∈ V X ⁢ ⁢ p ~ ⁡ ( x i ❘ y ) are emission probability constraints for state y and tr y = 1 - ∑ ∀ y i ∈ V Y ⁢ ⁢ p ⁡ ( y i ❘ y ) , t ⁢ r ~ y = 1 - ∑ ∀ y i ∈ V Y ⁢ ⁢ 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′.