{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:00:41Z","timestamp":1777705241713,"version":"3.51.4"},"reference-count":12,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,3,2]]},"abstract":"<jats:p>Graph convolutional networks (GCNs), which are capable of effectively processing graph-structural data, have been successfully applied in text classification task. Existing studies on GCN based text classification model largely concerns with the utilization of word co-occurrence and Term Frequency-Inverse Document Frequency (TF\u2013IDF) information for graph construction, which to some extent ignore the context information of the texts. To solve this problem, we propose a gating context-aware text classification model with Bidirectional Encoder Representations from Transformers (BERT) and graph convolutional network, named as Gating Context GCN (GC-GCN). More specifically, we integrate the graph embedding with BERT embedding by using a GCN with gating mechanism to enable the acquisition of context coding. We carry out text classification experiments to show the effectiveness of the proposed model. Experimental results shown our model has respectively obtained 0.19%, 0.57%, 1.05% and 1.17% improvements over the Text-GCN baseline on the 20NG, R8, R52, and Ohsumed benchmark datasets. Furthermore, to overcome the problem that word co-occurrence and TF\u2013IDF are not suitable for graph construction for short texts, Euclidean distance is used to combine with word co-occurrence and TF\u2013IDF information. We obtain an improvement by 1.38% on the MR dataset compared to Text-GCN baseline.<\/jats:p>","DOI":"10.3233\/jifs-201051","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T12:13:03Z","timestamp":1611922383000},"page":"4331-4343","source":"Crossref","is-referenced-by-count":30,"title":["A gating context-aware text classification model with BERT and graph convolutional networks"],"prefix":"10.1177","volume":"40","author":[{"given":"Weiqi","family":"Gao","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6604-0951","authenticated-orcid":false,"given":"Hao","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi, China"},{"name":"Xinjiang Provincial Key Laboratory of Multilingual Information Technology, Urumqi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-201051_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/1500000011","article-title":"Opinion mining and sentiment analysis","volume":"2","author":"Pang","year":"2008","journal-title":"Foundations and Trends\u00ae in Information Retrieval"},{"key":"10.3233\/JIFS-201051_ref3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TCBB.2018.2849968","article-title":"Natural language processing for EHR-based computational phenotyping","volume":"16","author":"Zeng","year":"2018","journal-title":"IEEE\/ACM transactions on computational biology and bioinformatics"},{"issue":"9","key":"10.3233\/JIFS-201051_ref8","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1007\/s42452-019-1116-x","article-title":"Test scheduling for system on chip using modified firefly and modified abc algorithms","volume":"1","author":"Chandrasekaran","year":"2019","journal-title":"SN Applied Sciences"},{"key":"10.3233\/JIFS-201051_ref10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"nature"},{"key":"10.3233\/JIFS-201051_ref11","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"10.3233\/JIFS-201051_ref14","doi-asserted-by":"crossref","first-page":"7370","DOI":"10.1609\/aaai.v33i01.33017370","article-title":"Graph convolutional networks for text classification,","volume":"33","author":"Yao","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/JIFS-201051_ref16","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s13042-010-0001-0","article-title":"Understanding bag-of-words model: a statistical framework","volume":"1","author":"Zhang","year":"2010","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"10.3233\/JIFS-201051_ref17","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"10.3233\/JIFS-201051_ref19","first-page":"45","article-title":"Support vector machine active learning with applications to text classification","volume":"2","author":"Tong","year":"2001","journal-title":"Journal of machine learning research"},{"key":"10.3233\/JIFS-201051_ref20","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"Bengio","year":"2003","journal-title":"Journal of machine learning research"},{"key":"10.3233\/JIFS-201051_ref32","first-page":"4","article-title":"Graph attention networks","volume":"1050","author":"Velickovic","year":"2018","journal-title":"stat"},{"key":"10.3233\/JIFS-201051_ref42","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"}],"container-title":["Journal of Intelligent &amp; 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