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Neural Processing Letters
Erschienen in: Neural Processing Letters 6/2022

10.05.2022

Simplified-Boosting Ensemble Convolutional Network for Text Classification

verfasst von: Fang Zeng, Niannian Chen, Dan Yang, Zhigang Meng

Erschienen in: Neural Processing Letters | Ausgabe 6/2022

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Abstract

Graph convolutional network (GCN) has a strong ability to extract the global feature but neglects the order of the words, thus leading to its weak effect on short text classification. In contrast, convolutional neural network (CNN) can capture the local contextual information within a sentence. There are few methods that can effectively classify both long text and short text. Therefore, we propose an ensemble convolutional network by combining GCN and CNN. In our method, GCN catches the global information and CNN extracts local features. Besides, we propose a simplified boosting algorithm, which makes CNN learn the samples misclassified by GCN again to improve classification performance and reduce the training time of the network. The results on four benchmark datasets show that our framework achieves better performance than other state-of-the-art methods with less memory.
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Literatur
1.
Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, et al (2018) Relational inductive biases, deep learning, and graph networks. arXiv:​1806.​01261
2.
Cheng W, Greaves C, Warren M (2006) From n-gram to skipgram to concgram. Int J Corpus Linguist 11(4):411–433CrossRef
3.
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATH
4.
Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:​181004805
5.
Elakkiya E, Selvakumar S, Velusamy RL (2020) Textspamdetector: textual content based deep learning framework for social spam detection using conjoint attention mechanism. J Ambient Intell Human Comput 12:1–16
6.
Gao H, Chen Y, Ji S (2019) Learning graph pooling and hybrid convolutional operations for text representations. In: The world wide web conference, pp 2743–2749
7.
8.
9.
James F (2005) The elements of statistical learning: data mining, inference and prediction. Math Intell 27(2):83–85
10.
11.
12.
13.
Kuznetsov V, Mochalov V, Mochalova A (2015) Ontological-semantic text analysis and the question answering system using data from ontology. In: 2016 18th International conference on advanced communication technology (ICACT). IEEE, pp 651–658
14.
Li F, Zhang M, Fu G, Qian T, Ji D (2016) A bi-lstm-rnn model for relation classification using low-cost sequence features. arXiv:​160807720
15.
Li Q, Peng H, Li J, Xia C, Yang R, Sun L, Yu PS, He L (2020) A survey on text classification: from shallow to deep learning. arXiv:​200800364
16.
Liang J, Deng Y, Zeng D (2020) A deep neural network combined cnn and gcn for remote sensing scene classification. IEEE J Sel Top Appl Earth Observ Remote Sens 13:4325–4338CrossRef
17.
Lin Y, Meng Y, Sun X, Han Q, Kuang K, Li J, Wu F (2021) Bertgcn: transductive text classification by combining gcn and bert. arXiv:​210505727
18.
Liu G, Li B, Hu W, Yang J (2013) Horror text recognition based on generalized expectation criteria. In: international conference on intelligent science and big data engineering. Springer, pp 136–142
19.
Liu P, Qiu X, Huang X (2016) Recurrent neural network for text classification with multi-task learning. arXiv:​160505101
20.
Lu Z, Du P, Nie JY (2020) Vgcn-bert: augmenting bert with graph embedding for text classification. In: European conference on information retrieval. Springer, pp 369–382
21.
Novotnỳ V, Ayetiran EF, Štefánik M, Sojka P (2020) Text classification with word embedding regularization and soft similarity measure. arXiv:​200305019
22.
Peng H, Li J, He Y, Liu Y, Bao M, Wang L, Song Y, Yang Q (2018) Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In: Proceedings of the 2018 world wide web conference, pp 1063–1072
23.
Peng H, Li J, Wang S, Wang L, Gong Q, Yang R, Li B, Yu P, He L (2019) Hierarchical taxonomy-aware and attentional graph capsule rcnns for large-scale multi-label text classification. IEEE Trans Knowl Data Eng 33(6):2505–2519
24.
Shao K, Zhang Z, He S, Bo X (2020) Dtigccn: prediction of drug-target interactions based on gcn and cnn. In: 2020 IEEE 32nd international conference on tools with artificial intelligence (ICTAI). IEEE, pp 337–342
25.
Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631–1642
26.
Tong X, Wu B, Wang S, Lv J (2018) A complaint text classification model based on character-level convolutional network. In: 2018 IEEE 9th international conference on software engineering and service science (ICSESS). IEEE, pp 507–511
27.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv:​170603762
28.
Wang SI, Manning CD (2012) Baselines and bigrams: Simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the association for computational linguistics (volume 2: short papers), pp 90–94
29.
Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International conference on machine learning, PMLR, pp 6861–6871
30.
Xu B, Guo X, Ye Y, Cheng J (2012) An improved random forest classifier for text categorization. JCP 7(12):2913–2920
31.
Xu D, Ruan C, Korpeoglu E, Kumar S, Achan K (2020) Inductive representation learning on temporal graphs. arXiv:​200207962
32.
Yadav RK, Jiao L, Granmo OC, Goodwin M (2021) Enhancing interpretable clauses semantically using pretrained word representation. In: Proceedings of the fourth BlackboxNLP workshop on analyzing and interpreting neural networks for NLP, pp 265–274
33.
Yao L, Mao C, Luo Y (2019) Graph convolutional networks for text classification. Proc AAAI Conf Artif Intell 33:7370–7377
34.
Zaheer M, Guruganesh G, Dubey A, Ainslie J, Alberti C, Ontanon S, Pham P, Ravula A, Wang Q, Yang L, et al. (2020) Big bird: transformers for longer sequences. arXiv:​200714062
35.
Zhang YD, Satapathy SC, Guttery DS, Górriz JM, Wang SH (2021) Improved breast cancer classification through combining graph convolutional network and convolutional neural network. Inf Process Manag 58(2):102439CrossRef
36.
Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57–81CrossRef
37.
Zhu H, Koniusz P (2021) Simple spectral graph convolution. In: International conference on learning representations
38.
Zukov-Gregoric A, Bachrach Y, Minkovsky P, Coope S, Maksak B (2017) Neural named entity recognition using a self-attention mechanism. In: 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI). IEEE, pp 652–656
Metadaten
Titel
Simplified-Boosting Ensemble Convolutional Network for Text Classification
verfasst von
Fang Zeng
Niannian Chen
Dan Yang
Zhigang Meng
Publikationsdatum
10.05.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 6/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10843-4

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