Experiments | For the error-driven policy, we collected unidentified unknown words using 10-fold cross validation on the training set, as previously described in Section 3. |
Experiments | Table 9: Comparison of averaged F1 results (by 10-fold cross validation ) with previous studies on CTB 3.0. |
Experiments | Unfortunately, Zhang and Clark’s experimental setting did not allow us to use our error-driven policy since performing 10-fold cross validation again on each main cross validation trial is computationally too expensive. |
Policies for correct path selection | 0 Divide the training corpus into ten equal sets and perform 10-fold cross validation to find the errors. |
Policies for correct path selection | After ten cross validation runs, we get a list of the unidentified unknown words derived from the whole training corpus. |
Policies for correct path selection | Note that the unidentified unknown words in the cross validation are not necessary to be infrequent words, but some overlap may exist. |
Baseline Approaches | This amounts to using three folds for training and one fold for development in each cross validation experiment. |
Dataset | Since we will perform 5-fold cross validation in our experiments, we also show the number of reports labeled with each shaper under the “F” columns for each fold. |
Evaluation | Micro-averaged 5-fold cross validation results of this baseline for all 14 shapers and for just 10 minority classes (due to our focus on improving minority class prediction) are expressed as percentages in terms of precision (P), recall (R), and F-measure (F) in the first row of Table 4. |
Evaluation | Table 4: 5-fold cross validation results. |
Our Bootstrapping Algorithm | Whichever baseline is used, we need to reserve one of the five folds to tune the parameter k in our cross validation experiments. |
Experiments of Parsing | Here we tried the corpus weighting technique for an optimal combination of CTB, CDTfs and parsed PDC, and chose the relative weight of both CTB and CDTfs as 10 by cross validation on the development set. |
Our Two-Step Solution | The number of removed trees will be determined by cross validation on development set. |
Our Two-Step Solution | The value of A will be tuned by cross validation on development set. |
Recognition as a Generation Task | (2008), we perform 10-fold cross validation . |
Results and Discussion | Table 5: Results of English abbreviation generation with fivefold cross validation . |
Results and Discussion | Concerning the training time in the cross validation , we simply chose the DPLVM for comparison. |