Experiments | We used 10-fold cross validation for all tests. |
Experiments | Table 2 compares the performance using 10-fold cross validation . |
Experiments | Table 2: Accuracy for SO-PMI with different dataset sizes, the spin model, and the random walks model for 10-fold cross validation and 14 seeds. |
Automated Classification | 5.2 Cross Validation Experiments |
Automated Classification | We performed 10-fold cross validation on our dataset, and, for the purpose of comparison, we also performed 5-fold cross validation on C) Séaghdha’s (2007) dataset using his folds. |
Automated Classification | To assess the impact of the various features, we ran the cross validation experiments for each feature type, alternating between including only one |
Evaluation | This comprises 108K sentences from the data made available by the University of Leipzig4 + 5600 sentences from the training data of each fold during cross validation . |
Evaluation | We perform a 5-fold cross validation taking 4/5 of the data as training and 1/5 as test data. |
Evaluation | Baseline P190: We ran Moses (Koehn et al., 2007) using Koehn’s training scriptslo, doing a 5-fold cross validation with no reordering“. |
Unsupervised Mining of Personal and Impersonal Views | xmm =< P(x'l=1),P(x'l=2),P(x'l=3) > In our experiments, we perform stacking with 4-fold cross validation to generate meta-training data where each fold is used as the development data and the other three folds are used to train the base classifiers in the training phase. |
Unsupervised Mining of Personal and Impersonal Views | 4-fold cross validation is performed for supervised sentiment classification. |
Unsupervised Mining of Personal and Impersonal Views | Also, we find that our performances are similar to the ones (described as fully supervised results) reported in Dasgupta and Ng (2009) where the same data in the four domains are used and 10-fold cross validation is performed. |
Experiments | We estimated the ROUGE metric using 10-fold cross validation . |
Experiments | Each corpus was then subjected to 10-fold cross validation , and the average results for training and testing were calculated. |
Experiments | Table 3: Results of 10-fold cross validation ENG HEB MULT Train 0.4483 0.5993 0.5205 Test 0.4461 0.5936 0.5027 |
Additional experiments | Because cross validation is applied, errors are always measured on testing subsets that are disjoint from the corresponding training subsets. |
Experimental design | We use tenfold cross validation for the experiments. |
Experimental design | These are learning experiments so we also use tenfold cross validation in the same way as with CRF++. |