This is done by computing an accuracy in the 10-fold cross validation .

Table 2: 10-fold cross validation results for three feature categories and their combination, for classifiers trained on English SVO and AN training sets.

ACC column reports an accuracy score in the 10-fold cross validation .

The values of various parameters were selected using 10-fold Cross Validation on

5, plots the 10-fold cross validation performance of the models with increasing values of H for the two datasets.

Figure 5: Cross validation performances of the two models with increasing number of categories.

Figure 7: Value precision vs. recall for 10-fold cross validation on TempEval-3 Dev and WikiWars Dev.

Figure 7 shows the precision vs. recall of the resolved values from 10-fold cross validation of TempEval-3 Dev and WikiWars Dev.

We also manually categorized all resolution errors for end-to-end performance with 10-fold cross validation of the TempEval-3 Dev dataset,

Figure 7 shows the results of a 10-fold cross validation on the 200-review dataset (light grey bars show the accuracy of the model trained without using transition cue features).

Table 4 shows the results obtained by 10-fold cross validation .

Each pairwise comparison is evaluated on a testing dataset with 10-fold cross validation .

Each value is the average over different test sets of fivefold cross validation .

Each value is the average over different test sets of fivefold cross validation .

This assumption is natural, considering the situation of an in-domain test or cross validation

When training and testing on the same corpus, we run a 10-fold cross validation .

One part is used as the development dataset; the rest are used for 10-fold cross validation .

The reported figures come from 10-fold cross validations on different corpora.