It maximizes the probability of getting the entire DA sequence correct , but it does not necessarily find the DA sequence that has the most DA labels correct ( Dermatas and Kokkinakis 1995 ) .
automatic stochastic tagging of natural language texts
Evangelos Dermatas George Kokkinakis
The combination of likelihood and prior modeling , HMMs , and Viterbi decoding is fundamentally the same as the standard probabilistic approaches to speech recognition ( Bahl , Jelinek , and Mercer 1983 ) and tagging ( Church 1988 ) . It maximizes the probability of getting the entire DA sequence correct , but it does not necessarily find the DA sequence that has the most DA labels correct ( Dermatas and Kokkinakis 1995 ) . To minimize the total number of utterance labeling errors , we need to maximize the probability of getting each DA label correct individually , i.e. , we need to maximize P ( U11E ) for each i = 1 , ... , n .
It maximizes the probability of getting the entire DA sequence correct , but it does not necessarily find the DA sequence that has the most DA labels correct ( @@CITATION ) .