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Multi-Class Document Classification Using Lexical Ontology-Based Deep Learning +  
Footnote †: Copyright** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))). **[PERSON] 1,2,*(c)**, [PERSON] 1(c) and [PERSON] 3(c)  
Footnote 26: https://www. boundaries will also increase. Therefore, it will be difficult for the algorithm to solve the problem. This is particularly difficult if the data is imbalanced. In a study conducted with the aim of increasing classification success rate, it was emphasized that reducing the size of the feature vector is important for increasing the success of the model. In the study, size reduction algorithms were divided into feature selection and feature extraction algorithms [2]. Feature extraction methods use algebraic transformations to reduce high-dimensional feature vectors to lower-dimensional spaces. Feature extraction algorithms can be divided into two types: linear and nonlinear algorithms [3]. These algorithms perform data transformation using optimization techniques. The most important method is Principal Component Analysis (PCA) [4], which produces new features. Developments in deep learning methods have resulted in developments in the field of text classification. Experimental studies have been carried out with the aim of increasing the success of classification models, especially with the emergence of the BERT model. In one study, the authors reported that methods using attention mechanisms such as BERT have the ability to capture contextual information present in the document. Within the scope of that study, the VGCNBERT model was proposed, which combines the capability of BERT with a Vocabulary Graph Convolution Network (VGCN). In their experiments, the best results were obtained when using the two-class SST-2 dataset, with an F1-Score of 91.93. The use of WordNet in future studies was also suggested by the authors, as the vocabulary graph can provide useful global information for BERT [5]. Therefore, it can be concluded that the use of WordNet could be beneficial. In some recent studies, the WordNet lexical ontology and BERT language model were used together to perform document classification, where the role of WordNet was as a source of semantic knowledge, such as with respect to word embeddings, e.g., path2 vec and wnet2 vec, while that of BERT was to extract the local feature information of the documents and to classify them [5,6]. In contrast to the articles described above, in this study, we propose a new model of WordNet lexical ontology and the BERT language model in which WordNet is used for dimension reduction using lexicography instead of domain ontology with the aim of increasing the success of the classification model on an imbalanced, multi-class dataset containing seven classes. In the experiments carried out in this study, WordNet is applied before classification by machine learning algorithms and BERT. The advantage of our new hybrid model is that it reduces the feature vector size, catches the semantic similarities of the words in the sentence, and provides higher classification success on unstructured, imbalanced and high-dimensional multi-class documents. The contributions of this article are manifold, as detailed below. 1. Evaluation of the effect of using the WordNet ontology for feature dimension reduction on classification success on imbalanced and multi-class (seven class labels) data. 2. Comparison of the performance of machine learning and deep learning classification algorithms when used on a multi-class imbalanced dataset with different imbalance ratios. 3.