## 6 Conclusions and Future Work  
In this paper, we conducted a series of experiments to evaluate the effect of lexical ontology and classification models with the aim of increasing classification success on multi-class and imbalanced datasets.  
In order to research the effect of lexical ontology on the success of classification models, we conducted experiments using two different categories of model: machine learning models and deep learning models. In the machine learning category, RF, SVM and MLP models were trained together with the BoW, TF-IDF, Word2 Vec and Doc2 Vec word embedding methods. Afterwards, these models were retrained with the same word embedding methods using WordNet. The highest success in this category was achieved when using the RF model together with WordNet and Doc2 Vec, with an accuracy of 93.01%. Experiments were then performed using BERT, which belongs to the deep learning category. Here, our dataset was first used to train BERT and then it was retrained with WordNet and the experiments were performed. The highest success rate in these experiments, an accuracy of 93.77%, was obtained when using WordNet and the pretrained BERT as a hybrid model. WordNet was used for feature dimension reduction, and lexicography files were used to group words with the same meaning and repeating words, and we showed that using a lexical ontology for dimension reduction increased the classification success rate. It was observed that feature dimension reduction using WordNet increased the success rate on the seven-class imbalanced dataset for both traditional machine learning classification models and the deep-learning-based BERT and DistilBERT classification models. However, BERT was more successful than the other classical methods, because it provides deeper learning. Additionally, DistilBERT, which is a derivative of BERT, provided good results when used in conjuction with WordNet. It also worked faster than BERT. We believe that this study can motivate researchers to conduct document classification research using lexical ontology, and our model can be applied in a variety of text classification tasks, especially in cases where unstructured data are present and there are multiple classes to classify.  
In future works, we aim to extend these experiments using other BERT models such as ALBERT, RoBERTa, XLNet, etc., with WordNet and various multi-class imbalanced datasets. We will also conduct experiments to address class imbalance in multi-class datasets using various methods. Furthermore, since the Graph Convolutional Network (GCN) has recently achieved successful results in text classification, it will be used together with and independently of BERT in order to measure its success on multi-class imbalanced datasets.  
Figure 10: ROC curves formed when using BERT alone and when using WordNet and BERT together. (**a**) BERT+WordNet ROC Curve; (**b**) DistilBERT+WordNet ROC Curve.  
**Author Contributions:** Conceptualization, I.Y. and A.G.; methodology, I.Y., A.G. and M.Z.; software, I.Y.; validation, I.Y., A.G. and M.Z.; formal analysis, I.Y. and A.G.; investigation, I.Y. and M.Z.; resources, I.Y.; data curation, I.Y.; writing--original draft preparation, I.Y., A.G. and M.Z.; writing--review and editing, I.Y., A.G. and M.Z.; visualization, I.Y.; supervision, A.G.; project administration, A.G.; funding acquisition, I.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by TUBITAK (2011-C Priority Areas Graduate Programme Scholarship).  
Not applicable.  
**Data Availability Statement:** Available on request.  
We wish to thank TUBITAK (2011-C Priority Areas Graduate Programme Scholarship) and Turkcell Group Company Digital Educational Technologies Inc. for financial support.  
The authors declare no conflict of interest.