A method for sequence tagging medical patient records includes providing a labeled corpus of sentences taken from a set of medical records, initializing generative parameters θ and discriminative parameters {tilde over (θ)}, providing a functional LL−C×Penalty, where 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 ) , ⁢ Penalty = ∑ y ∈ V Y ⁢ ( em y 2 + tr y 2 + e ⁢ ⁢ m ~ y 2 + t ⁢ ⁢ r ~ y 2 ) , where emy=1−Σ∀xiεVXp(xi|y), e{tilde over (m)}y=1−Σ∀xiεVX{tilde over (p)}(xi|y) are emission probability constraints, try=1−Σ∀yiεVYp(yi|y), t{tilde over (r)}y=1−Σ∀yiεVY{tilde over (p)}(yi|y) are transition probability constraints, and extracting gradients of LL−C×Penalty with respect to the transition and emission probabilities and solving θk*,{tilde over (θ)}k*that maximize LL−C×Penalty, initializing a new iteration with θk*,{tilde over (θ)}k* and incrementing C and repeating until solutions have converged, where parameters θ,{tilde over (θ)} are the probabilities that a new sentence X′ is labeled as Y′.
System and method for text tagging and segmentation using a generative/discriminative hybrid hidden markov model