Abstract

In this paper, The Arabic text presents various difficulties, as text mining in Arabic is challenging due to its inflectional solid and derivational nature. The applications of Arabic information extraction are still in their early stages and require an improved approach to take advantage of its potential. Information extraction requires a general method that can work on the text regardless of its domain (e.g., biomedical, sport, economics, etc.) and capture all its information. Machine learning techniques have been applied to make information extraction systems more portable. Machine learning aims to develop algorithms to assist or replace domain experts in knowledge engineering situations. By using learning algorithms that rely on open information extraction to automate information retrieval processes such as document classification, the modeling can reduce the workload of information workers and minimize the inconsistencies introduced by human error, focusing on the research domain and problem.

References

• S. Ali, H. Mousa, and M. Hussien, “A Review of Open Information Extraction Techniques,” IJCI. Int. J. Comput. Inf., vol. 6, no. 1, pp. 20–28, 2019.
• A. Téllez-valero, M. Montes-y-gómez, and L. Villaseñor-pineda, “A Machine Learning Approach to Information Extraction,” no. February, 2005.
• M. Al-Ayyoub, A. Nuseir, K. Alsmearat, Y. Jararweh, and B. Gupta, “Deep learning for Arabic NLP: A survey,” J. Comput. Sci., vol. 26, no. November, pp. 522–531, 2018.
• R. Bemthuis et al., “ScienceDirect Business Business rule rule extraction extraction using using decision decision tree tree machine machine learning learning techniques : study into smart returnable transport techniques : A case study into smart returnable transport items it,” Procedia Comput. Sci., vol. 220, pp. 446–455, 2023.
• S. M. A. El-Morsy, M. Hussein, and H. M. Mousa, “Arabic open information extraction system using dependency parsing,” Int. J. Electr. Comput. Eng., vol. 12, no. 1, pp. 541–551, 2022.
• S. Larabi Marie-Sainte, N. Alalyani, S. Alotaibi, S. Ghouzali, and I. Abunadi, “Arabic natural language processing and machine learning-based systems,” IEEE Access, vol. 7, pp. 7011–7020, 2019.
• M. Luna, E. D. Gennatas, L. H. Ungar, E. Eaton, E. S. Diffenderfer, and S. T. Jensen, “Building more accurate decision trees with the additive tree,” vol. 116, no. 40, 2019.
• S. El-Morsy, “, ‘Arabic Open Information Extraction -model by Machine Learning to extract relation’ Github.com. https://github.com/salsama/Implementation-of-arabic-open-information- extraction/blob/master/DT_model.py,” 2023.
• H. Yu, “Relation Extraction with BERT-based Pre-trained Model,” pp. 1382–1387, 2020.
• A. R. Extraction and L. Framework, “REEL :,” 2014.
• S. Mohamed, M. Hussien, and H. M. Mousa, “ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction,” Int. J. Inf. Technol. Comput. Sci., vol. 13, no. 1, pp. 54–61, 2021.
• M. Dragoni, M. Federici, and A. Rexha, “An unsupervised aspect extraction strategy for monitoring real-time reviews stream,” Inf. Process. Manag., vol. 56, no. 3, pp. 1103–1118, 2019.
• Z. Q. Geng, G. F. Chen, Y. M. Han, G. Lu, and F. Li, “Semantic relation extraction using sequential and tree-structured LSTM with attention,” Inf. Sci. (Ny)., vol. 509, pp. 183–192, 2020.
• K. M. Almunirawi and A. Y. A. Maghari, “A Comparative Study on Serial Decision Tree Classification Algorithms in Text Mining,” vol. 7, no. 4, pp. 754–760, 2016.
• D. Han, Z. Zheng, H. Zhao, S. Feng, and H. Pang, “Span-based single-stage joint entity-relation extraction model,” PLoS One, vol. 18, no. 2 February, pp. 1–14, 2023.
• A. Elnagar, R. Al-Debsi, and O. Einea, “Arabic text classification using deep learning models,” Inf. Process. Manag., vol. 57, no. 1, p. 102121, 2020.
• M. A. R. Abdeen, S. AlBouq, A. Elmahalawy, and S. Shehata, “A closer look at arabic text classification,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 11, pp. 677–688, 2019.
• R. Zhou, H. N. Nguyen, and I. Sasase, “Packet scheduling for cellular relay networks by considering relay selection, channel quality, and packet utility,” J. Commun. Networks, vol. 11, no. 5, pp. 464–472, 2009.
• D. Hutchison et al., “Computational Linguistics and Intelligent Text Processing 18th,” in 18th International Conference, CICLing 2017, 2017.
• R. D. Brown, LNAI 8082 - Text, Speech, and Dialogue, no. September. 2013.
• I. Boujelben, S. Jamoussi, A. Ben Hamadou, and A. Ben Hamadou, “A hybrid method for extracting relations between Arabic named entities,” J. King Saud Univ. - Comput. Inf. Sci., vol. 26, no. 4, pp. 425–440, 2014.
• S. E. Kramdi et al., “Approche générique pour l ’ extraction de relations à partir de textes To cite this version : HAL Id : hal-00384415 Approche générique pour l ’ extraction de relations à partir de textes,” 2009.
• I. Boujelben, S. Jamoussi, and A. Ben Hamadou, “Enhancing Machine Learning Results for Semantic Relation Extraction,” pp. 337–342, 2013.
• A. Ibm and T. A. Road, “Mining Association in Large Databases,” pp. 207–216, 1993.
• F. Peng and A. McCallum, “Information extraction from research papers using conditional random fields,” Inf. Process. Manag., vol. 42, no. 4, pp. 963–979, 2006.