| Approach | Here, we apply the edit distance to measure how best the path is. |
| Approach | This means the best path is the word sequence path in the FST which has the smallest edit distance compared with the to-be-scored transcription’s word sequences . |
| Approach | EDcost is the edit distance from the transcription to the paths which start at state 0 and end at the end |
| Abstract | We compare two parsing models for temporal dependency structures, and show that a deterministic non-projective dependency parser outperforms a graph-based maximum spanning tree parser, achieving labeled attachment accuracy of 0.647 and labeled tree edit distance of 0.596. |
| Evaluations | Tree Edit Distance In addition to the UAS and LAS the tree edit distance score has been recently introduced for evaluating dependency structures (Tsarfaty et al., 2011). |
| Evaluations | The tree edit distance score for a tree 7r is based on the following operations A E A : A = {DELETE, INSERT, RELABEL}: |
| Evaluations | Taking the shortest such sequence, the tree edit distance is calculated as the sum of the edit operation costs divided by the size of the tree (i.e. |