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courses:rg:non-projective-dependency-parsing-using-spanning-tree-algorithms [2011/04/19 10:00]
popel
courses:rg:non-projective-dependency-parsing-using-spanning-tree-algorithms [2011/04/19 12:09] (current)
abzianidze
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   * In the paper, a non-projective parser is understood as a parser allowing non-projective dependency parse trees along with projective dependency parse trees, in contrast to a projective parser, which forbids non-projective dependency parse trees. Also under the space of non-projective trees, authors mean the union space of both non-projective and projective trees.   * In the paper, a non-projective parser is understood as a parser allowing non-projective dependency parse trees along with projective dependency parse trees, in contrast to a projective parser, which forbids non-projective dependency parse trees. Also under the space of non-projective trees, authors mean the union space of both non-projective and projective trees.
   * The score of dependency trees are commonly represented as the sum of the scores of all edges in the tree, and the score of an edge is a dot product weight vector and feature vector (containing information about nodes - words).   * The score of dependency trees are commonly represented as the sum of the scores of all edges in the tree, and the score of an edge is a dot product weight vector and feature vector (containing information about nodes - words).
-  * Chu-Liu-Edmonds algorithm for finding maximum spanning trees takes in general <​latex>​O(n^3)</​latex>​ time but for dense graphs Tarjan (1997) gave an efficient implementation of the algorithm with <​latex>​O(n^2)</​latex>​. The former ​implementation is used by authors. ​         ​+  * Chu-Liu-Edmonds algorithm for finding maximum spanning trees takes in general <​latex>​O(n^3)</​latex>​ time but for dense graphs Tarjan (1997) gave an efficient implementation of the algorithm with <​latex>​O(n^2)</​latex>​. The latter ​implementation is used by authors. ​         ​
   * In the training phase, two modified versions of the Margin Infused relaxed Algorithm (MIRA) are used: Single-best MIRA and Factored MIRA. The reason of the modifications is to lower the time complexity of the training.   * In the training phase, two modified versions of the Margin Infused relaxed Algorithm (MIRA) are used: Single-best MIRA and Factored MIRA. The reason of the modifications is to lower the time complexity of the training.
   * Experiments are done on the Czech PDT. In particular, on entire PDT (Czech-A) and on 23% portion of PDT including only non-projective dependency trees (Czech-B). The introduced algorithm is competing to other 3 dependency parsers (2 projective and 1 pseudo-projective). ​     ​   * Experiments are done on the Czech PDT. In particular, on entire PDT (Czech-A) and on 23% portion of PDT including only non-projective dependency trees (Czech-B). The introduced algorithm is competing to other 3 dependency parsers (2 projective and 1 pseudo-projective). ​     ​

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