# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 1 Introduction
For example, a text graph is constructed from a corpus of word co-occurrence relations and semantic dictionaries, as shown in Figure 1. Here \"This movie can be described in one word: wonderful.\" is a sentence in the corpus. Taking \"wonderful\" as the center, the blue circle represents the aggregation of one-level neighbor node information, and the green circle represents the aggregation of two-level neighbor node information. Among them, \"wonderful\" is used to modify \"movie\" in the sentence, and it is very useful for analyzing the expression of the text as a whole. However, the two word nodes \"movie\" and \"wonderful\" in Figure 1 are not directly related, but there should be a certain dependency between them. Graph Convolutional Network only aggregates direct neighbor node information, so one layer of GCN can only capture short-distance dependencies of words in sentences. Capturing long-range dependencies between words such as \"movie\" and \"wonderful\" can be solved by increasing the number of GCN layers. However, current research shows that multi-layer graph convolutional networks for text classification tasks incur high space complexity. At the same time, increasing the number of network layers will also make the local features converge to similar values. To overcome this problem, we introduce dependencies when building the text graph. Dependency syntax is mainly concerned with the relationship between words (sentence components) in a sentence, and it is not affected by the physical location of the components. By introducing dependencies, short-range dependencies and long-range dependencies can be well utilized while also providing syntactic constraints and partially reducing the number of GCN layers. However, due to the characteristics of the graphs, when learning word features, graph convolutional networks do not effectively capture the word order that is very useful for them, resulting in the loss of contextual semantic information of the text. For example, \"You have a lot of hard work.\", \"You have to work hard.\", where the word \"work\" will change meaning depending on its position in the contextual sentence. The graph formed by the words in these two sentences cannot reflect the order between the words, so it cannot represent the characteristics of word order changes. Obviously, this is very important for understanding the meaning of the sentence. In addition, the graph convolutional network cannot distinguish polysemy; for example, \"She's grown into quite an individual.\", \"So this individual came up.\", the meaning of the word \"individual\" expresses different semantics depending on the context. In the first sentence, it is the meaning of \"person with personality\", and in the second sentence, it has the meaning of \"weird person\". In this case, if not differentiated, it may affect the understanding of the text, thereby affecting the classification effect. Leveraging multilayer multi-head self-attention and positional word embeddings, BERT mainly focuses on local contiguous word sequences that provide local contextual information. By pre-training a large amount of text data in different fields, BERT can well infer the meaning that polysemous words should express in the current context. However, BERT may have difficulty interpreting global information about vocabulary. To improve the above-mentioned problems, this paper proposes an improved model (IMGCN) for text classification. On the basis of Vocabulary Graph Convolution (VGCN) [15], a semantic dictionary (WordNet) and dependencies are introduced, and the features of GCN and BERT are further learned through residual bi-layer BiLSTM with an attention mechanism [17]. By building dependencies, GCNs can be made to capture long-range dependencies of words in sentences more efficiently. WordNet was introduced to provide more useful connections between words. At the same time, the BERT model is used to extract the local feature information of a text.