# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 3 Proposed Method
### Text Graph  
#### 3.1.1 Vocabulary Graph  
In this paper, we first use a semantic dictionary (Wordnet) and Normalized Pointwise Mutual Information (NPMI) to construct a text graph. For constructing text graphs using word co-occurrence relationships, normalized pointwise mutual information (NPMI) is generally used to calculate edge weights between two word nodes, which is a common  
Figure 2: The architecture of IMGCN. measure of word association. However, the calculation of NPMI depends on the corpus. If the probability of some words appearing in the corpus is low, this method may lead to a small calculation result of NPMI, and the similarity of two words is judged to be low or not. For example, the word \"abundant\" has a very low probability of appearing in the corpus. Let P (word and other words) be the co-occurrence probability between words. When calculating the co-occurrence probability of \"abundant\" and other words, the values of P (abundant and other words) and P (abundant) are very small; even if it is 0, it will result in a very small value of NPMI, so in the end, \"abundant\" and other words may be judged as having little or no semantic relevance. In addition, there are a lot of connections between words, and the most direct connection is the synonymous relationship that exists between certain words. For example, the words \"abundant\" and \"plentiful\" have very similar meanings and can be used interchangeably in most cases. If two words are synonymous, then only constructing a text graph through word co-occurrence relationship may lack this part of word relationship information. If this synonymous relationship can be used, the relationship information of words can be enriched, thereby improving the accuracy of the calculation. The semantic dictionary describes the different semantic relations between words. Therefore, we jointly construct a text graph using word co-occurrence relations and semantic lexicon. For the selection of the semantic dictionary, we use WordNet. WordNet describes a network of semantic relationships between words. For every content word in the language, it gives all possible word meanings. WordNet organizes words in a dictionary using semantic and lexical relations, where semantic relations represent the degree of association between the semantic items of two words. Noums, verbs, adverbs, and adjectives are each organized into a network of synonyms, each set of synonyms representing a basic semantic concept, and these sets are also connected by various relationships. Since a word can have multiple meanings, it is necessary to compare the meanings between the two words. Therefore, we first use WordNet to expand synonyms and then use the WordNet-based Wup method to calculate the semantic similarity between words in the two synonym sets and calculate the average of all similarity scores. This value is the final similarity score of the two words. The larger the calculated value, the higher the semantic similarity between them. Among them, the \"Wup\" method [28] is a similarity measurement method based on the path structure proposed by [PERSON] and [PERSON]. The similarity is calculated according to the similarity of the meaning of the words and the position of the synonym sets in the hypernym tree relative to each other. Therefore, for two words i and j, we first calculate the edge weights between two words using the NPMI method. If the calculated NPMI value is small, the WordNet-based method is used for calculation, which can minimize the problems caused by the low frequency of some words in the corpus, and at the same time, add more word relationship information. Formally, the edge weight between node i and node j is defined as:  
\[\mathrm{A_{ij}}=\left\{ \begin{array}{ll}\mathrm{NPMI(i,j)}&\mathrm{i,j are words, NPMI(i,j)>0}\\ \mathrm{WordSim(i,j)}&0.5\leq\mathrm{WordSim(i,j)\leq 1,NPMI(i,j)<0}\\ 1&\mathrm{i=j}\\ 0&\mathrm{otherwise}\end{array} \right.