# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 3 Proposed Method
### Text Graph
#### 3.1.2 Dependency Graph  
Based on the above two relational compositions, graph convolutional networks can capture short-range dependencies of words in sentences but cannot capture long-range dependencies at the same time. Therefore, we introduce dependencies to construct another text graph to overcome the above problems. In addition, in order to obtain the inclusion relationship between documents and words while introducing the dependencies between words, we combine the two relationships. The edge weights between general document nodes and word nodes are calculated using the TF-IDF algorithm. TF-IDF is the word frequency-inverse text frequency index. The term frequency (TF) is the number of times a word appears in the document, and the inverse text frequency (IDF) reflects the general importance of the feature word in the corpus. However, the TF-IDF method lacks the combination of textual semantics, which is useful for understanding textual content. To overcome this problem, we refer to [29], introduce dependencies, and use an improved edge weight calculation method to understand and optimize text features. Dependency syntax is mainly concerned with the relationship between the various words in a sentence, and it is not affected by the physical location of the components [30]. Since the importance and relevance of various dependencies in a sentence are not the same, first, the importance of words to sentences, texts, and even categories is determined according to the different dependencies between words and predicates in terms of word meaning. Then, according to the importance of different components to the sentence, the sentence composition is divided into eight levels. The results of dependency analysis are usually represented by a labeled directed graph. For example, as shown in Figure 3, \"root\" represents the central word \"has\". Examples of the relationship between the performance head word and its subsidiary words are \"has\" and \"performances\". The result of dependency analysis shows that the relationship between these two words is a direct object (dobj, direct object) relationship. After classifying the text features in the data set according to the dependency relationship, this paper uses the following dependency-based TFIDF weight calculation method [29]. The specific steps are as follows:  
For the word i, we calculate the number of times that the word i appears in the text and set it as n. Then, according to the result of the dependency syntax analysis implemented by Stanford Parser, it is obtained that the word i belongs to the m-th (\(1\ \leq\ \text{m}\ \leq\ \text{n}\)) sentence component in the text. Furthermore, according to paper [29], classify the m-th occurrence of word i in the text into k\({}_{\text{i,m}}\) level, and assign weight \(\text{w}_{\text{i,m}}\) to it. The calculation process is shown in Formula (4):  
\[\text{w}_{\text{i,m}} =2\cos[(\frac{\text{k}_{\text{i,m}}}{8})^{\lambda}\times\frac{ \pi}{2}] \tag{4}\] \[\text{TF}_{\text{i}} =\sum_{\text{m}=1}^{\text{n}}\text{w}_{\text{i,m}}\] (5) \[\text{TF-IDF}_{\text{i}} =\frac{\text{TF}_{\text{i}}}{\text{s}}\times\text{ log}(\frac{\text{D}}{\text{P}_{\text{i}}}+0.01) \tag{6}\]  
Then the improved frequency TF\({}_{\text{i}}\) with the weight of word i in the text is calculated by Formula (5). For word i, the improved TFIDF\({}_{\text{i}}\) weight based on the dependency relationship is as shown in Formula (6). Where s represents the total number of words in the text where the word i is located, pi represents the number of texts containing the word i, and D represents the total number of texts in the dataset. Where \(\lambda\) is a parameter, which is used to adjust the weight gap between feature levels, and the range is [0,1]. Calculate the weight of the edge between the word node and the document node according to the above method, and obtain the adjacency matrix A, as shown in Formula (7).