And four, the number of situations in the PAN 2016 corpus is a great deal

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Function set D2V (1-gram) D2V (1 + 2-grams) D2V (1 + 2 + 3-grams) Character 3-grams Bag-of-Words Function set D2V (1-gram) D2V (1 + 2-grams) D2V (1 + 2 + 3-grams) Character 3-grams Bag-of-Words Age LR-NP 66.45 71.05 69.73 65.78 65.78 LR-NP 59.87 63.15 65.13 57.23 60.52 LR-WP 66.45 74.34 70.39 67.76 65.78 Gender LR-WP 66.44 69.73 69.07 61.84 56.57 S like flowcharts, often asked question sheets (FAQ) and drug packaging SVM-NP 56.57 61.84 65.78 59.21 61.84 SVM-WP 69.07 71.05 71.71 62.50 55.26 SVM-NP 68.42 71.05 68.42 66.44 65.13 SVM-WP 69.73 72.36 70.39 67.ten 65.Table 6: Obtained final results (accuracy, ) for age and gender classification around the PAN author profiling 2015 Spanish traini.And 4, the number of instances within the PAN 2016 corpus is substantially higher than in the PAN 2015. Tables 5?1 present the age and gender classes accuracy obtained around the PAN 2015 and PAN 2016 corpora utilizing two distinct classifiers (LR and SVM) with and devoid of preprocessing. Right here "LR-NP" is logistic regression devoid of preprocessing; "LR-WP," logistic regression with preprocessing; "SVM-NP," SVM with out preprocessing; "SVM-WP," SVM with preprocessing; "D2V," Doc2vec. The very best results for each classifier (with/without preprocessing) are in bold. The very best results for every single feature set are underlined. Note that when conducting experiments around the PAN 2015 corpus for the age class, we only consider the Spanish plus the English datasets, since for Dutch and Italian this class is currently not accessible. For the same cause, for the PAN 2016 corpus, we provide the outcomes for the age and gender classes for the English and Spanish languages, although, for the Dutch language, the results are provided only for the gender class. For the majority of circumstances, Doc2vec technique outperforms the baseline approaches. However, for the Dutch and Italian datasets of PAN 2015, the character 3-grams method supplies higher accuracy. This can be explained by the fact title= s13415-015-0346-7 thatComputational Intelligence and NeuroscienceTable five: Obtained outcomes (accuracy, ) for age and gender classification around the PAN author profiling 2015 English education corpus under 10-fold cross-validation. Function set D2V (1-gram) D2V (1 + 2-grams) D2V (1 + 2 + 3-grams) Character 3-grams Bag-of-Words Function set D2V (1-gram) D2V (1 + 2-grams) D2V (1 + 2 + 3-grams) Character 3-grams Bag-of-Words Age LR-NP 66.45 71.05 69.73 65.78 65.78 LR-NP 59.87 63.15 65.13 57.23 60.52 LR-WP 66.45 74.34 70.39 67.76 65.78 Gender LR-WP 66.44 69.73 69.07 61.84 56.57 SVM-NP 56.57 61.84 65.78 59.21 61.84 SVM-WP 69.07 71.05 71.71 62.50 55.26 SVM-NP 68.42 71.05 68.42 66.44 65.13 SVM-WP 69.73 72.36 70.39 67.ten 65.Table 6: Obtained outcomes (accuracy, ) for age and gender classification on the PAN author profiling 2015 Spanish traini.