I call a woman a christian, that works from morning till night, to get bread for her family

88 The formula works as follow: 25

a. I call a woman a christian, that works from morning till night, to get bread for her family

, and is never heard to scold or mutter. b. I call a woman a christian, that works from morning till night, to get her family bread , and is never heard to scold or mutter. verb : get theme : bread : non-pronound; indefinitef 1 beneficiary : her family : animateb; non-givenc; non-locale 1 2 log scale= 2-1=+1a constant+βx1+βx2+βx3+… +βxn e In p constant+βx1+βx2+βx3+… +βxn 1+e expconstant+βx1+βx2+βx3+… +βxn 1+ expconstant+βx1+βx2+βx3+… +βxn -1.766+0.573a+2.249b+3.563c -1.909d+1.090e-1.369f 1-1.766+0.573a+2.249b+3.563c -1.909d+1.090e-1.369f 89 -1.766+0.5731+2.2491+3.5632 -1.9092+1.0902-1.3692 1 -1.766+0.5731+2.2491+3.5632 -1.9092+1.0902-1.3692 = 0.97825 The result shows the tendency of the instance to take benefactive PP construction. The number shows that it is more than 0.5 to take benefactive PP, so the model predicts that the occurrence is in benefactive PP construction. In this instance, the predicted construction matches the observed construction. In the observed data, the following instance appears in the double object construction. The model will try to predict the construction using the six significant features. 26 a. of beer with their father, who had been an honest Dutch cooper, and got himself a comfortable fortune , which was all his children seemed to have inherited from b. of beer with their father, who had been an honest Dutch cooper, and got a comfortable fortune for himself , which was all his children seemed to have inherited from verb : got theme : a comfortable fortune : non-pronound; indefinitef 1 2 3 beneficiary : himself : animateb; givenc; non-locale 1 90 log scale= 1-3= -2 a constant+βx1+βx2+βx3+… +βxn e In p constant+βx1+βx2+βx3+… +βxn 1+e expconstant+βx1+βx2+βx3+… +βxn 1+ expconstant+βx1+βx2+βx3+… +βxn -1.766+0.573a+2.249b+3.563c -1.909d+1.090e-1.369f 1-1.766+0.573a+2.249b+3.563c -1.909d+1.090e-1.369f -1.766+0.573-2+2.2491+3.5631 -1,9092+1,0902-1,3692 1 -1.766+0.573-2+2.2491+3.5631 -1.9092+1.0902-1.3692 = 0.18603 The result shows that the tendency of the instance to take benefactive PP construction is 0.18603 which is less than 0.5. Thus, the model predicts that the occurrence is in double object construction. Once again, the predicted construction matches the observed construction. The figure below shows the process of predicting the expected PP realization. In this model, the column „probability to take PP‟ shows the percentage of the instance tendency to take benefactive PP construction. When the probability is more than 0.5, the instance tends to take benefactive PP 91 construction. Conversely, when the value is less than 0.5, the instance tends to take double object construction. The column expected is the nominal values predicted PP and DO which are converted into numbers. The column observed is taken from the observed construction of ditransitivity. When the expected and observed matches, the accuracy of prediction is written accurate. It works vice versa. Figure 4.5 Excel processing of PP realization prediction Compared to the previous model, in which, all fourteen features are included, this model has higher -2 log likehood, slightly less chi-square statistic, and slightly less Nagelkerke R square. The model with fourteen features has -2 log likehood of 193.177 while this model with six features has -2 log likehood of 207.708. The chi-square statistic of the model with fourteen features is 342.706, whereas this model has chi-square statistic of 328,175. The model with fourteen features has Nagelkerke R square of 0.780, while this model has Nagelkerke R 92 square of 0.758. It tells us that the new model, indeed, slightly less accurate than the previous one. Yet, the declining of the accuracy is hardly noticeable since it only helps to correctly predict an instance more out of 100 occurrences. With the new model which considers only six features and an intercept, the new model is able to accurately predict 90 out of 100 instances. The model is tested in some ways and proven to be valid. To test the internal validity of the formula and to find the accuracy of the model to the unseen data, the ten-fold validation is used. The researcher breaks the data set into 10 folds of instances, and then new model is made. The model is computed from 90 of the total data. The computed data consists of the second until the tenth folds. The corpus probability formula which is obtained then is used to test the rest 10 instances, which is the fold number one. The next model is made from the folds one, three until tenth. The formula is tested on fold number two. The test works this way until ten models out of the 10 folds were made. The accuracy is derived from the mean of accuracy of the 10 models. This test will show how well the model can predict the unseen data within the internal data set. The result shows that it still has relatively high percentage of overall model accuracy 89.5 compared to the majority baseline of 70.1. The first model reaches 82.5 accuracy when it is tested to first fold. Then the following models consecutively show the accuracy of 87.5, 85, 92.5, 95, 85, 87.5, 95, 90, and 95. The mean of the accuracy of those ten models is 89.5. See the figure below see also appendix 5: 93 Figure 4.6 The percentage of the ten-fold cross-validation accuracy In addition, to test the external validity of the model, the model is used to predict the occurrences of instances in other data set. The model is applied in 40 instances which are taken from Time Magazine Corpus and is able to correctly predict 34 out of 40. It shows that the external validity of the model is still relatively high, which is 85, when applied to other similar situations in real world, in this case in Time Magazine Corpus situationsee appendix 6.Two examples are brought up from the Time Magazine Corpus data set. 27

a. do an he says he wants to study bugs-dats nuts-and Ive just got him a job