Natural Language Processing Based on a Text Graph Convolutional Network
Tipo
Artigo de evento
Data de publicação
2023
Periódico
Lecture Notes in Networks and Systems
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0
Autores
Pereira V.C.M.
de Castro L.N.
de Castro L.N.
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© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Deep Learning (DL) has been one of the preferred techniques for Natural Language Processing (NLP) applications. Due to its nature, a text can be better represented in a graph structure, when compared with the classical feature-based representations. Therefore, several researchers have explored the use of Graph Neural Networks (GNN) for text analysis. GNNs show excellent results in text classification tasks, given their property of capturing contextual and global information in a corpus. The Text Graph Convolutional Network (TGCN) showed the ability to outperform traditional NLP methods in benchmark classification tasks. However, this method has a very high memory cost for the text graph construction. By exploring the results of text representations, we propose a new method to generate a text graph, capable of influencing the result of the TGCN, leading to a reduced use of memory.