Machine Learning and Natural Language Processing in Domain Classification of Scientific Knowledge Objects: A Review

1. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. 26(2), xiii–xxiii (2002)

   Google Scholar 

2. Zimbra, D., Abbasi, A., Zeng, D., Chen, H.: The state-of-the-art in Twitter sentiment analysis. ACM Trans. Manag. Inf. Syst. 9(2), 1–29 (2018)

   Article  Google Scholar 

3. Boudad, N., Faizi, R., Oulad Haj Thami, R., Chiheb, R.: Sentiment analysis in Arabic: a review of the literature. Ain Shams Eng. J. 9(4), 2479–2490 (2018)

   Google Scholar 

4. Romanov, A., Lomotin, K., Kozlova, E.: Application of natural language processing algorithms to the task of automatic classification of Russian scientific texts. Data Sci. J. 18(1), 1–17 (2019)

   Google Scholar 

5. Nurfikri, F.S., Adiwijaya: A comparison of neural network and SVM on the multi-label classification of Quran verses topic in English translation. J. Phys. Conf. Ser. 1192(1), 2479–2490 (2019)

   Google Scholar 

6. Kim, S.-W., Gil, J.-M.: Research paper classification systems based on TF-IDF and LDA schemes. Hum. Cent. Comput. Inf. Sci. 9(1), 30 (2019)

   Article  Google Scholar 

7. Saini, S., Singh, S.P., Agarwal, R.: Augmented machine learning ensemble extension model for social media health trends predictions. Int. J. Recent Technol. Eng. 8(2), 482–486 (2019). Special Issue 7

   Article  Google Scholar 

8. Cheng, Y., Ye, Z., Wang, M., Zhang, Q.: Document classification based on convolutional neural network and hierarchical attention network. Neural Netw. World 29(2), 83–98 (2019)

   Article  Google Scholar 

9. Semberecki, P., MacIejewski, H.: Deep learning methods for subject text classification of articles. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, pp. 357–360 (2017)

   Google Scholar 

10. Hernández, F.L., Pérez, E.V., Granados, J.J.R., Crespo, R.G.: A Nondisturbing service to automatically customize notification sending using implicit-feedback. Sci. Program. 2019, 17 (2019)

    Google Scholar 

11. wei Fan, J., et al.: Part-of-speech tagging for clinical text: wall or bridge between institutions? AMIA Annu. Symp. Proc. 2011, 382–391 (2011)

    Google Scholar 

12. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)

    MATH  Google Scholar 

13. Soumya George, K., Joseph, S.: Text classification by augmenting bag of words (BOW) representation with co-occurrence feature. IOSR J. Comput. Eng. 16(1), 34–38 (2014)

    Article  Google Scholar 

14. Nicholson, C.: A Beginner’s Guide to Bag of Words & TF-IDF (2019). https://pathmind.com/wiki/bagofwords-tf-idf. Accessed 18 Jan 2020

15. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, pp. 1532–1543 (2014)

    Google Scholar 

16. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings (2013)

    Google Scholar 

17. Wiki, A.I.: A Beginner’s Guide to Word2Vec and Neural Word Embeddings | Skymind (2019). https://pathmind.com/wiki/word2vec. Accessed 18 Jan 2020

18. Caragea, C., Bulgarov, F., Mihalcea, R.: Co-training for topic classification of scholarly data. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pp. 2357–2366 (2015)

    Google Scholar 

19. Bhaskaran, S.K., Sreejith, C., Rafeeque, P.C.: Neural networks and conditional random fields based approach for effective question processing. Procedia Comput. Sci. 143, 211–218 (2018)

    Article  Google Scholar 

20. Ghumade, T.G., Deshmukh, R.A.: A document classification using NLP and recurrent neural network. Int. J. Eng. Adv. Technol. 8(6), 632–636 (2019)

    Article  Google Scholar 

21. Jing, R.: A self-attention based LSTM network for text classification. J. Phys. Conf. Ser. 1207, 012008 (2019)

    Article  Google Scholar 

22. Kaplan, D., Tokunaga, T., Teufel, S.: Citation block determination using textual coherence. J. Inf. Process. 24(3), 540–553 (2016)

    Google Scholar 

23. Khabiri, E., Gifford, W.M., Vinzamuri, B., Patel, D., Mazzoleni, P.: Industry specific word embedding and its application in log classification, pp. 2713–2721 (2019)

    Google Scholar 

24. Montenegro, C., Santana, R., Lozano, J.A.: Data generation approaches for topic classification in multilingual spoken dialog systems. In: ACM International Conference Proceeding Series, pp. 211–217 (2019)

    Google Scholar 

25. Romanov, A.Y., Lomotin, K.E., Kozlova, E.S., Kolesnichenko, A.L.: Research of neural networks application efficiency in automatic scientific articles classification according to UDC. In: Proceedings of the 2016 International Siberian Conference on Control and Communications, SIBCON 2016 (2016)

    Google Scholar 

26. Tang, D., Qin, B., Liu, T.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pp. 1422–1432 (2015)

    Google Scholar 

27. Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pp. 1422–1432 (2015)

    Google Scholar 

28. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations ofwords and phrases and their compositionality. In: Advances in Neural Information Processing Systems (2013)

    Google Scholar 

29. Forman, G.: An extensive empirical study of feature selection metrics for text classification (2003)

    Google Scholar 

30. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, pp. 1746–1751 (2014)

    Google Scholar 

31. Nahar, K.M.O., Al, A.F., Eroud, M.B., Al-Akhras, A.M.: SAP: standard Arabic profiling toolset for textual analysis. Int. J. Mach. Learn. Comput. 9(2), 222–229 (2019)

    Article  Google Scholar 

32. Ratkovic, Z., Golik, W., Warnier, P.: Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach. BMC Bioinf. 13, S11 (2012)

    Article  Google Scholar 

33. Yue, X., Di, G., Yu, Y., Wang, W., Shi, H.: Analysis of the combination of natural language processing and search engine technology. Procedia Eng. 29, 1636–1639 (2012)

    Article  Google Scholar 

34. Zhou, J., Li, B.C., Chen, G.: Automatically building large-scale named entity recognition corpora from Chinese Wikipedia. Front. Inf. Technol. Electron. Eng. 16(11), 940–956 (2015)

    Article  Google Scholar 

35. Nahar, K.M.O., Alhindawi, N., Al-Hazaimeh, O.M., Alkhatib, R.M., Al-Akhras, A.M.: NLP and IR based solution for confirming classification of research papers. J. Theor. Appl. Inf. Technol. 96(16), 5269–5279 (2018)

    Google Scholar 

36. Ulumudin, G.I., Adiwijaya, A., Mubarok, M.S.: A multilabel classification on topics of qur’anic verses in English translation using K-Nearest Neighbor method with Weighted TF-IDF. J. Phys: Conf. Ser. 1192(1), 1–4 (2019)

    Google Scholar 

Download references