1. A method, performed by a processor of a computer system, utilizing an expanded Conditional Random Field (eCRF) model, the method comprising: performing actions, by an eCRF manager, relating to eCRF applications, the actions comprising: accessing an eCRF model that includes modeling of labels with long-distance dependencies; constructing an expanded searching lattice comprising nodes and edges; extracting features for the nodes and edges in the searching lattice, wherein the features include feature values, and wherein the feature values are based on a distance from the labels with long-distance dependencies; computing, by the processor, probabilities of the nodes and the edges in the searching lattice; determining a label sequence using the searching lattice; and outputting the label sequence.
1. A method, performed by a processor of a computer system, utilizing an expanded Conditional Random Field (eCRF) model, the method comprising: performing actions, by an eCRF manager, relating to eCRF applications, the actions comprising: accessing an eCRF model that includes modeling of labels with long-distance dependencies; constructing an expanded searching lattice comprising nodes and edges; extracting features for the nodes and edges in the searching lattice, wherein the features include feature values, and wherein the feature values are based on a distance from the labels with long-distance dependencies; computing, by the processor, probabilities of the nodes and the edges in the searching lattice; determining a label sequence using the searching lattice; and outputting the label sequence. 3. The method of claim 1 , wherein the eCRF model is expressed by: p(Y,X)=Π t= 1 T exp{Σ k=1 K λ k f k (y t , y t-1 , x t )+Σ l=1 L θ l g l (y t , d t r , x t )} where T is the length of the sequence, K is the number of feature functions, λ k is the weight for k-th feature functions f k , which model a relationship among current input feature x t , a previous output label y t-i and a current output label y t , and where L is a number of dynamic feature functions, θ l is a weight for l-th feature functions g l , and d t r are a distance of the current label from/to a previous/next specific output label r.