Abstract
Natural language processing has been studied extensively worldwide and has been implemented into various applications, including text classification. Especially, the significant development of social networking platforms has led to a considerable increase in data. Thus, it becomes the fertile data domain to carry out a series of studies on text classification. Various studies on this task are conducted in many languages but still have many limitations with Vietnamese. This is why we aim to do this study to classify Vietnamese texts from two Vietnamese benchmark datasets. Despite many studies on machine learning models in this study, any research work using facts and rules in a deductive database to classify Vietnamese text classification has not been studied. In particular, we design a system architecture based on facts and rules in a deductive database for text classification in Vietnamese. Our experiments show our results are positive on two Vietnamese datasets. The best performances from the experiments achieve 93.18% of F1-score for the UIT-ViNames dataset, 76.79% and 69.96% for the sentiment detection and the topic classification on the UIT-VSFC dataset, respectively. Although the experimental results are not better than the previous studies, these results are the premise for developing solutions for natural language processing problems on the deductive database, a successful pilot in implementing text classification on the Prolog-based deductive database.
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Abdallah, S., Shaalan, K., Shoaib, M.: Integrating rule-based system with classification for Arabic named entity recognition. In: Gelbukh, A. (ed.) CICLing 2012. LNCS, vol. 7181, pp. 311–322. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28604-9_26
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.J.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Language in Social Media (LSM 2011), pp. 30–38 (2011)
Gallaire, H., Minker, J., Nicolas, J.M.: Logic and databases: a deductive approach. Read. Artif. Intell. Databases. 231–247 (1989)
Garg, S., Ramakrishnan, G.: BAE: BERT-based adversarial examples for text classification. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6174–6181. Association for Computational Linguistics, November 2020. https://doi.org/10.18653/v1/2020.emnlp-main.498, https://www.aclweb.org/anthology/2020.emnlp-main.498
Hartmann, J., Huppertz, J., Schamp, C., Heitmann, M.: Comparing automated text classification methods. Int. J. Res. Market. 36(1), 20–38 (2019)
Ho, V.A., et al.: Emotion recognition for Vietnamese social media text. arXiv preprint arXiv:1911.09339 (2019)
Huynh, H.D., Do, H.T.T., Van Nguyen, K., Nguyen, N.L.T.: A simple and efficient ensemble classifier combining multiple neural network models on social media datasets in Vietnamese. arXiv preprint arXiv:2009.13060 (2020)
Jacovi, A., Sar Shalom, O., Goldberg, Y.: Understanding convolutional neural networks for text classification. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 56–65. Association for Computational Linguistics, Brussels, November 2018. https://doi.org/10.18653/v1/W18-5408, https://www.aclweb.org/anthology/W18-5408
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683
Julián-Iranzo, P., Sáenz-Pérez, F.: A fuzzy datalog deductive database system. IEEE Trans. Fuzzy Syst. 26(5), 2634–2648 (2018)
Kenyon-Dean, K., et al.: Sentiment analysis: it’s complicated! In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 1886–1895 (2018)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)
Li, W., Xu, H.: Text-based emotion classification using emotion cause extraction. Expert Syst. Appl. 41(4), 1742–1749 (2014)
MacAvaney, S., Yao, H.R., Yang, E., Russell, K., Goharian, N., Frieder, O.: Hate speech detection: challenges and solutions. PLoS ONE 14(8), e0221152 (2019)
Madabushi, H.T., Lee, M.: High accuracy rule-based question classification using question syntax and semantics. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1220–1230 (2016)
Morawietz, F.: Chart parsing and constraint programming. In: COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics (2000)
Narayanan, V., Arora, I., Bhatia, A.: Fast and accurate sentiment classification using an enhanced Naive Bayes model. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 194–201. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41278-3_24
Nguyen, D.T., Van Nguyen, K., Pham, T.T.: Implementing a subcategorized probabilistic definite clause grammar for Vietnamese sentence parsing. Int. J. Nat. Lang. Comput. 2(4), 27 (2013)
Nguyen, H.T., et al.: VLSP shared task: sentiment analysis. J. Comput. Sci. Cybern. 34(4), 295–310 (2018)
Nguyen, L.T., Nguyen, K.V., Nguyen, N.L.T.: Constructive and toxic speech detection for open-domain social media comments in Vietnamese. arXiv preprint arXiv:2103.10069 (2021)
Nguyen, P.X., Hong, T.T., Van Nguyen, K., Nguyen, N.L.T.: Deep learning versus traditional classifiers on Vietnamese students’ feedback corpus. In: 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), pp. 75–80. IEEE (2018)
Nguyen, Q.T., Nguyen, T.L., Luong, N.H., Ngo, Q.H.: Fine-tuning BERT for sentiment analysis of Vietnamese reviews (2020)
Nguyen, V.D., Van Nguyen, K., Nguyen, N.L.T.: Variants of long short-term memory for sentiment analysis on Vietnamese students’ feedback corpus. In: 2018 10th International Conference on Knowledge and Systems Engineering (KSE), pp. 306–311. IEEE (2018)
Schneider, K.-M.: Techniques for improving the performance of Naive Bayes for text classification. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 682–693. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30586-6_76
To, H.Q., Nguyen, K.V., Nguyen, N.L.T., Nguyen, A.G.T.: Gender prediction based on Vietnamese names with machine learning techniques (2020)
Tran, T.K., Phan, T.T.: Capturing contextual factors in sentiment classification: an ensemble approach. IEEE Access 8, 116856–116865 (2020). https://doi.org/10.1109/ACCESS.2020.3004180
Van Huynh, T., Nguyen, V.D., Van Nguyen, K., Nguyen, N.L.T., Nguyen, A.G.T.: Hate speech detection on Vietnamese social media text using the bi-GRU-LSTM-CNN model. arXiv preprint arXiv:1911.03644 (2019)
Van Huynh, T., Van Nguyen, K., Nguyen, N.L.T., Nguyen, A.G.T.: Job prediction: from deep neural network models to applications. In: 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), pp. 1–6. IEEE (2020)
Van Nguyen, K., Nguyen, V.D., Nguyen, P.X., Truong, T.T., Nguyen, N.L.T.: UIT-VSFC: Vietnamese students’ feedback corpus for sentiment analysis. In: 2018 10th International Conference on Knowledge and Systems (KSE), pp. 19–24. IEEE (2018)
van Thin, D., Nguyen, V.D., van Nguyen, K., Nguyen, N.L.T.: A transformation method for aspect-based sentiment analysis. J. Comput. Sci. Cybern. 34(4), 323–333 (2018)
Wang, J.H., Liu, T.W., Luo, X., Wang, L.: An LSTM approach to short text sentiment classification with word embeddings. In: Proceedings of the 30th Conference on Computational Linguistics and Speech Processing (ROCLING 2018), pp. 214–223 (2018)
Williams, M.H., et al.: Prolog and deductive databases. Knowl.-Based Syst. 1(3), 188–192 (1988)
Yu, S., Su, J., Luo, D.: Improving BERT-based text classification with auxiliary sentence and domain knowledge. IEEE Access 7, 176600–176612 (2019)
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Nguyen, K.V., Van Huynh, T., Nguyen, A.GT. (2021). A Novel Perspective of Text Classification by Prolog-Based Deductive Databases. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_12
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