Natural Language Processing Based on a Text Graph Convolutional Network

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dc.contributor.authorPereira V.C.M.
dc.contributor.authorde Castro L.N.
dc.date.accessioned2024-03-12T19:12:41Z
dc.date.available2024-03-12T19:12:41Z
dc.date.issued2023
dc.description.abstract© 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.
dc.description.firstpage1
dc.description.lastpage10
dc.description.volume583 LNNS
dc.identifier.doi10.1007/978-3-031-20859-1_1
dc.identifier.issn2367-3389
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34242
dc.relation.ispartofLecture Notes in Networks and Systems
dc.rightsAcesso Restrito
dc.subject.otherlanguageDeep Learning
dc.subject.otherlanguageGraph Neural Network
dc.subject.otherlanguageNLP
dc.titleNatural Language Processing Based on a Text Graph Convolutional Network
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-85144950254
local.scopus.updated2024-12-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85144950254&origin=inward
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