First, we implement a chart-based dynamic programming parser for the 2nd-0rdered MST model, and develop a training procedure based on the perceptron algorithm with averaged parameters (Collins, 2002).

J iang and Liu (2009) resort to a dynamic programming procedure to search for a completed projected tree.

Jiang and Liu (2009) refer to alignment matrix and a dynamic programming search algorithm to obtain better projected dependency trees.

Follow the edge based factorization method (Eisner, 1996), we factorize the score of a dependency tree s(x, y) into its dependency edges, and design a dynamic programming algorithm to search for the candidate parse with maximum score.

In this work, however, we still adopt the more general, bottom-up dynamic programming algorithm Algorithm 1 in order to facilitate the possible expansions.

The preferred sequence is determined by using dynamic programming and beam search.

Our algorithm efficiently searches for the best sequence of sentences by using dynamic programming and beam search.

To alleviate this, we find an approximate solution by adopting the dynamic programming technique of the Held and Karp Algorithm (Held and Karp, 1962) and beam search.

In the search procedure, our dynamic programming based algorithm retains just the hypothesis with maximum score among the hypotheses that have the same sentences and the same last sentence.

The 1-1 alignment problem can be formulated as a dynamic programming problem to find the maximum score of alignment, given a probability table of aligning letter and phoneme as a mapping function.

The dynamic programming recursion to find the most likely alignment is the following:

It combines a dynamic programming alignment algorithm with an appropriate scoring scheme for computing phonetic similarity on the basis of multivalued features.