## 3 Methods
### Graph Construction  
There exist different methods for graph construction in the context of text classification. An important, known method is to construct a graph for a corpus based on word co-occurrence and document-word relations. This graph is a heterogeneous graph that includes word nodes and document nodes, allowing the modeling of global word co-occurrence and the adaptation of graph convolution techniques [20]. After constructing the graph, the next step is to create initial feature vectors for each node in the graph based upon pre-trained models, namely BERT and ParsBERT. It is worth noting that all the unique words obtained from the pre-processing stage, along with all the documents, altogether form the set of nodes of our graph. Edges are weighted and defined to belong to one of three groups. One group consists of those edges between document-word pairs based upon word occurrence in documents. The second group consists of those edges between word-word pairs based upon word co-occurrence in the entire corpus. The third group consists of those edges between document-document pairs based upon word co-occurrence in the two documents. As shown in Equation (1), the weight \(A_{i,j}\) of the edges \((i,j)\) is defined using the _term frequency-inverse document frequency (TF-IDF) [8] measure_ (for document-word edges) and the _point-wise mutual information (PMI) measure_ (for word-word edges). The PMI measure captures thesemantic correlation between words in the corpus. Moreover, _Jaccard similarity_ is used to define the edge weight by calculating the similarity between two documents [21]. \[A_{i,j}= \begin{cases}PMI_{i,j}&\text{$i$ and $j$ are both words,}\\ TF-IDF_{i,j}&\text{$i$ is a doc and $j$ is a word,}\\ Jacard_{i,j}&\text{$i$ and $j$ are both documents,}\\ 1&\text{$i$}=j,\end{cases}  \tag{1}\]  
It is worth noting that these measurements (TF-IDF and PMI) are normalized in the sense that we actually consider and use the normalized symmetric adjacency matrix instead of the raw matrix \(A\). This method has been used in other earlier works and has also been experimentally shown to be useful. Each document is fed to the pre-trained model multi-lingual BERT or the ParsBERT model, resulting in a numerical vector representation. For each word, a min pooling operation is applied over the BERT or ParsBERT representations of the documents containing the word. More precisely, in the same manner as that of reference [21], given a word, the min pooling over the BERT representations of all documents containing that word gives rise to the representation of that word. Now, the feature vector associated to every node is defined. We now talk address the pre-trained models BERT and ParsBERT. These models have gained widespread popularity in text processing and natural language processing (NLP). They provide high-quality embeddings that serve as features for downstream tasks. They eliminate the need for manual feature engineering. The BERT vector, also known as BERT embedding, is in fact a high-dimensional vector that represents a text. This embedding is created by encoding words in a sentence using a pre-trained BERT model trained on a large dataset of textual data. The encoding process generates a fixed-length vector for each word in the sentence, which can then serve as input for other natural language processing models. The ParsBERT model, similarly, was pre-trained from diverse sources of Persian textual data. It is worth noting that, beside the BERT frameworks, there have been many other popular frameworks for finding representations of texts such as \"word mover's embedding\", \"Word2 vec\", etc. BERT is a language model widely used for a variety of natural language processing tasks, in particular tasks that require an understanding of the context, while some other frameworks, such as the mentioned word mover's embedding, focuses on word alignment and is useful for tasks involving semantic similarity or semantic distance.