###### Abstract  
This study compares the performance of graph convolutional neural network (GCN) models with conventional natural language processing (NLP) models for classifying scientific literature related to radio frequency electromagnetic field (RF-EMF). Specifically, the study examines two GCN models: BertGCN and the citation-based GCN. The study concludes that the model achieves consistently good performance when the input text is long enough, based on the attention mechanism of BERT. When the input sequence is short, the composition parameter \(\lambda\), which combines output values of the two subnetworks of BertGCN, plays a crucial role in achieving high classification accuracy. As the value of \(\lambda\) increases, the classification accuracy also increases. The study also proposes and tests a simplified variant of BertGCN, revealing performance differences among the models under two different data conditions by the existence of keywords. This study has two main contributions: (1) the implementation and testing of a variant of BertGCN and citation-based GCN for document classification tasks related to radio frequency electromagnetic fields publications, and (2) the confirmation of the impact of model conditions, such as the existence of keywords and input sequence length, in the original BertGCN. Although this study focused on a specific domain, our approaches have broader implications that extend beyond scientific publications to general text classification. document classification; BERT; graph convolution neural network; electromagnetic fields +
Footnote †: journal: _sciences_  
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of relevant information for subsequent analysis. For instance, if a study is in vivo, it is possible to glean details such as specific animal species and the number of animals used. However, these models are limited to analyzing individual documents and do not take into account the relationships between papers. To address this limitation, we propose a novel approach that utilizes a graph structure to explore the inter-document relationships among papers. Specifically, we apply this approach to the corpus of RF-EMF-related literature, which is relatively small compared to general document corpora and technical vocabulary. This study aims to improve existing document classification approaches by utilizing graph convolutional neural networks (GCN) [4]. In this approach, nodes in the graph network represent articles, and weighted edges capture relationships between them. We hypothesize that citation practices play a significant role in document classification [5; 6; 7]. In addition, we compare the performance of GCN with Transformer-based BERT, which is a standard model used in NLP classification tasks [8; 9]. For the experiment, we constructed two types of datasets: (a) abstracts of papers on RF-EMF and their corresponding in vivo/in vitro labels, and (b) citation data for the papers. While the citation information can be utilized to build a citation graph, a statistically connected graph can be created by harnessing the frequency and co-occurrence property between documents and words [10]. Our study evaluates the efficiency of both these approaches for document classification. Furthermore, we draw upon the PECO framework, a decision tree used for evaluating RF-EMF studies [1; 3], for the development of our work. The primary contributions of this study are as follows:  
* First, we implemented and tested a variant of BertGCN, called modified BertGCN, and a citation-based GCN for document classification tasks on RF-EMF-related publications. The modified BertGCN has a simplified architecture by removing the composition parameter of the original BertGCN. Additionally, we demonstrated how the citation relation of scientific publications can be utilized for document classification in the context of citation-based GCN.