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For epsilon-SVR the epsilon in the loss function was allowed to vary between 0. All optimization experiments are performed using the Gallop toolbox Desmet and Hoste Gallop provides the functionality to wrap a complex optimization problem as a genome and to distribute the computational load of the GA run over multiple processors or to a computing cluster. It is specifically aimed at problems involving natural language.
In this section, we present the results of our experiments for the regression and classification tasks. For each task, we first performed a baseline experiment Section 5. In the discussion of our results, we make a distinction between the readability prediction experiments performed on our two languages under consideration using only automatically derived features Section 5. We start each time by presenting the optimal results after which we discuss in close detail which features contributed most to the readability predictions. In Table 5 , we present the baseline results using LibSVM in a fold cross validation set-up for our two readability prediction tasks.
For both tasks, the default learner options were set and all available features were fed to the learners. For the regression task, we achieve a better result on the English data set, whereas the opposite seems to hold for the classification experiments—that is, both the binary and multiclass experiments on the Dutch data set achieve a superior accuracy score.
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As expected, the performance on the binary data sets is much higher than on the multiclass data sets. Table 6 gives an overview of the results of the two different rounds of optimization experiments that were conducted. On the left-hand side we present the results on the regression task, and on the right-hand side those of the binary and multiclass classification tasks.
The results of these two different rounds will be discussed separately. In the Round 1 experiments, LibSVM's hyperparameters were set to the default options and the focus was on selecting the optimal features for readability prediction in both languages. In a first set-up, variation between the ten different feature groups was allowed, and in the second set-up those features requiring deep processing were optimized individually. We observe a similar tendency in both prediction tasks.
Compared with the baselines Table 5 , better results are always achieved when performing feature selection. We also observe that for both tasks the best results are achieved with the individual feature selection optimization experiments, though the performance increase is moderate, which is not that remarkable given the inherent feature weighting in the greedy type of learning that SVMs perform.
In Round 2 similar experiments were performed, but this time LibSVM's hyperparameters were jointly optimized while selecting the optimal features. We observe that this setting results in the best results indicated in bold for both prediction tasks. If we have a closer look at the differences between both set-ups, joint feature groups versus joint individual features, we see that the differences in performance are moderate.
For the regression task, we observe for both languages a minimal difference of 0. For the classification tasks, these differences are more outspoken: For the English data set we achieve an increase of 0.
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For the Dutch data set, we achieve a performance increase of 0. As the latter experiments led to the best results, we will now discuss which features and which hyperparameters were selected in the fittest individuals. Because, at the end of a GA optimization run, the highest fitness score may be shared by multiple individuals having different optimal feature combinations or parameter settings, we also considered runner-up individuals to that elite as valuable solutions to the search problem.
When discussing the results of the GA experiments, we therefore refer to the k -nearest fitness solution set; these are the individuals that obtained one of the top k fitness scores, given an arithmetic precision e. Following Desmet , we used a precision of four significant figures and set k to three.
We will discuss which hyperparameters, and especially which features groups, were selected in both languages. The features are visualized using a color range: The closer to blue, the more this feature group was turned on and the closer to red, the less important the feature group was for reaching the optimal solution.
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The numbers within the cells represent the same information but percentagewise. In Figure 4 , we illustrate which feature groups were considered important using this color range.
What immediately draws our attention is the discrepancy between the regression and classification tasks in both languages. Apparently, the optimal regression results can be achieved with far fewer features: for both languages only the lexical i. For both the binary and multiclass classification tasks it is better to have more feature information available, especially for the multiclass experiments. Regarding those features requiring more complicated linguistic processing the deepsynt , ner , coref and srl features , we observe that these feature groups are always selected for the classification tasks in both languages.
Because the best results for the classification experiments were achieved when performing an individual selection of those features we made an additional analysis of the individual features that were or were not retained in those optimal set-ups. These are presented in Figure 5 , in which a black box refers to a selected feature, and a white box refers to a feature that was not selected.
When comparing our two languages under consideration, we observe that similar features are selected. The multiclass experiments reveal a similar tendency though here the coref features seem to beat to deep syntactic features when it comes to being selected in both languages. Also, most of the ner 5 versus 6 out of 7 and srl 15 and 14 out of 20 features are selected in both languages. This confirms that for the classification task the features requiring deep linguistic processing are important to achieve optimal performance.
For the regression experiments, we perform a similar analysis but go one step further in that we also analyze text correlates. These findings are presented in the next section. The next step, if included at all, is then to see which features come out as good predictors when performing machine learning experiments such as regression Pitler and Nenkova , or classification Feng et al. Interestingly, the most predictive features often do not overlap with those having the highest correlation Pitler and Nenkova We compute the Pearson correlation coefficient between all individual features and our regression data set, in which we have an absolute score for each individual text.
As we observed in our experiments, the optimal settings for regression did not require the activation of many feature groups in both languages see Figure 4.
We hope to shed more light on this by identifying text correlates. In our discussion we only report on features with a significant correlation coefficient i. Regarding the traditional features , we found that in both languages the four length-related features tradlen correlate with our regression data set; the features related to word-length show an especially stronger correlation. This brings us to the lexical features. For the Dutch data set, the perplexity of a given text when compared with our reference corpus i.
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For English, these language modeling features lexlm do not correlate. At the level of syntactic features , we make a division between shallow features computed based on PoS-tags shallowsynt and a deeper level based on dependency parsing deepsynt. However, for both languages at least one feature representing the five main part-of-speech classes nouns, adjectives, verbs, adverbs, and prepositions does correlate. For English, the average amount of function and content words also correlates. From the group of deep syntactic features, we see that for Dutch all six features correlate significantly and for English they all correlate but one.
This brings us to our final group of features, the semantic features. As to the named entity features ner , we again observe some differences between English and Dutch. Whereas for English especially the average amounts of entities and named entities correlate, for Dutch the overall percentages of entities and named entities in a document correlate more.
Finally, we considered the semantic role features srl. For Dutch, on the other hand, the total number of arguments and the Arg1 and Arg3 arguments correlate significantly together with three modifiers. In Figure 6 , we compare these results with the analysis of the feature groups coming from the optimal regression set-ups. A black cell means that a feature group was either selected in the optimal setting or found to correlate.
Those feature groups revealing similar tendencies two black or two white cells have been indicated in bold.
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For English, we observe that only five out of the ten feature groups show a similar tendency, whereas for Dutch seven out of the ten feature groups do. This implies that for our English data set there is a less outspoken link between features correlating and them being selected in the optimal regression experiments, which is in line with the results presented by Pitler and Nenkova Given that the optimal results were achieved while jointly optimizing both features and hyperparameters, we briefly list which hyperparameters were selected. For both languages a linear kernel was chosen and the cost-value ranges from 2 12 to 2 For the classification tasks we observe that for the binary task a linear kernel is preferred whereas for the multiclass task the default more complex RBF kernel.
C-values are slightly lower: 2 11 to 2 Another aspect of this research was to investigate in closer detail the contribution of those features requiring deep linguistic processing.
Though many advances have been made in NLP, the more difficult text-understanding tasks such as coreference resolution or semantic role labeling still achieve moderate performance rates. Implementing such features in a readability prediction system is thus risky as the automatically derived features might not truly represent the information at hand.
Because we have gold-standard deep syntactic and semantic information available for our Dutch readability data set, we were able to investigate in close detail their added value in predicting readability. In Table 7 , we present the baseline results using LibSVM in a fold cross validation set-up for our two readability prediction tasks. For the regression task we observe that relying on a feature space with gold-standard deep syntax and semantic features harms performance whereas for the classification tasks, especially for the multiclass experiments i.
Table 8 gives an overview of the results of the two different optimization rounds. On the left-hand side, we present the results on the regression task, and on the right-hand side those of the binary and multiclass classification tasks. The best individual results for the Dutch language are indicated in bold. We see that for both tasks these best results are achieved with the Dutch fully automatic feature space. We will start by discussing the results of the two different optimization rounds.
In the Round 1 experiments, we observe a different tendency in both prediction tasks. For the regression task, a set-up with gold-standard features never outperforms the results achieved with the fully automatic features. In the classification tasks, however, and especially in the multiclass experiments, relying on gold-standard deep syntactic and semantic features seems beneficial an increase of 2. In the second round , counterintuitively, we notice that the best results for both languages and both tasks are achieved with the fully automatic features. Because the only difference between the two data sets are the feature values of the deep syntactic and deep semantic feature groups, we had a close inspection of these particular features.
Figure 7 gives an overview of the feature groups which were considered important in the optimization. Again, the groups are visualized using the previously mentioned color range see Section 5. When relying on gold-standard deep syntactic and semantic information we observe that more feature groups are considered important for the regression task, 8 out of the 10 groups including deepsynt , ner , coref , and srl become selected versus 3 in the experiments where automatically derived features were used.
For the classification tasks the situation alters less, in the binary experiments one feature group appears more important tradlen , and in the multiclass experiments one semantic feature group even gets turned off coref in the gold standard. We make an additional analysis of the individual features that were or were not retained in the optimal set-ups, this comparison is presented in Figure 8. In the remainder of this section we zoom in on the classification experiments and in the next section we do the same for the regression experiments.