Machine Translation (MT) is a crucial application of Natural Language Processing (NLP). This MT technique automatic and based on computers. One of the most modern techniques adopted in MT is Machine Learning (ML). Over the past few years, ML has grown in popularity during MT process among researchers. Ambiguity is a major challenge in MT. Word Sense Disambiguation (WSD) is a common technique for solving the ambiguity problem. ML approaches are commonly used for the WSD techniques and are used for training and testing purposes. The outcome prediction of the test data gives encouraging results. Text classification is one of the most significant techniques for resolving the WSD. In this paper, we have analyzed some common supervised ML text classification algorithms and also proposed a “hybrid model” called “AmbiF.” We have compared the results of all analyzed algorithms with the proposed model “AmbiF. The analyzed supervised algorithms are Decision Tree (DT), Bayesian network, Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Random Forest (RF), and Logistic Regression (LR). The range of accuracy for all the algorithms that were examined is between sixty-eight and eighty-four percent. To improve the accuracy of the AmbiF model, we have merged the DT, SVM, and Naïve Bayes (NB)-classifier approach. For testing the model, we have used the ten-fold cross-validation test method. The AmbiF model’s accuracy has been reported eighty- five percent. Comparing the AmbiF model to all other analyzed supervised ML classification algorithms, it has also demonstrated great precision, recall, and F-score. Waikato Environment for Knowledge Analysis (WEKA)’s ML-tool is used to analyze the algorithms and the AmbiF model.

[1] Abraham A., Gupta B., Maurya A., Verma S., Husain M., and Ali A., “Naïve Bayes Approach for Word Sense Disambiguation System with a Focus on Parts-of-Speech Ambiguity Resolution,” IEEE Access, vol. 12, pp. 126668-126678, 2024. https://ieeexplore.ieee.org/document/10663552

[2] Alian M. and Awajan A., “Generating Sense Inventories for Ambiguous Arabic Words,” The International Arab Journal of Information Technology, vol. 18, no. 3A, pp. 446-451, 2021. https://doi.org/10.34028/iajit/18/3A/8

[3] Alloghani M., Al-Jumeily D., Mustafina J., Hussain A., and Aljaaf A., Supervised and Unsupervised Learning for Data Science, Springer, 2020. https://link.springer.com/chapter/10.1007/978-3- 030-22475-2_1

[4] Aung N., Soe K., and Thein N., “A Word Sense Disambiguation System Using Naïve Bayesian Algorithm for Myanmar Language,” International Journal of Scientific and Engineering Research, vol. 2, no. 9, pp. 1-6, 2011. https://www.uit.edu.mm/wp- content/uploads/2020/05/NTTA-4.pdf

[5] Bahadur P. and Chauhan D., “Machine Translation-A Journey,” in Proceedings of the Science and Information Conference, London, pp. 187-195, 2014. https://ieeexplore.ieee.org/document/6918189?de nied=

[6] Bates S., Hastie T., and Tibshirani R., “Cross- Validation: What does it Estimate and how Well does it Do it?,” Journal of the American Statistical Association, vol. 119, no. 546, pp. 1434-1445, 2024. https://doi.org/10.1080/01621459.2023.2197686

[7] Boyle B., Support Vector Machines: Data Analysis, Machine Learning and Applications, Nova Science Publishers, 2011.

[8] Brownlee J., “A Gentle Introduction to K-Fold Cross-Validation,” Machine Learning Mastery, 2018. https://scholar.google.com/citations?user=hVaJh RYAAAAJ&hl=en&oi=sra

[9] Chauhan N., Decision Tree Algorithm. Explained- KDnuggets, https://www.kdnuggets.com/2020/01/decision- tree-algorithm-explained.html, Last Visited, 2024.

[10] Dhar S., Roy K., Dey T., Datta P., and Biswas A., “A Hybrid Machine Learning Approach for Prediction of Heart Diseases,” in Proceedings of the 4th International Conference on Computing Communication and Automation, Greater Noida, pp. 1-6, 2018. https://ieeexplore.ieee.org/abstract/document/877 7531

[11] Escudero G., Marquez L., and Rigau G., “Naive Bayes and Exemplar-Based Approaches to Word Sense Disambiguation Revisited,” arXiv Preprint, vol. arXiv:cs/0007011, pp. 1-5, 2000. https://arxiv.org/abs/cs/0007011

[12] Frank E., Hall M., Holmes G., Kirkby R., Pfahringer B., Witten I., and Len T., Data Mining and Knowledge Discovery Handbook, Springer, 2010. 1140 The International Arab Journal of Information Technology, Vol. 21, No. 6, November 2024 https://link.springer.com/chapter/10.1007/978-0- 387-09823-4_66

[13] Gawande U., Kale S., and Thaokar C., Recent Advances in Material, Manufacturing, and Machine Learning, CRC Press, 2023.

[14] Guo G., Wang H., Bell D., Bi Y., and Greer K., “KNN Model-Based Approach in Classification,” in Proceedings of the OTM Confederated International Conferences, on the Move to Meaningful Internet Systems, Sicily, pp. 986-996, 2003. https://link.springer.com/chapter/10.1007/978-3- 540-39964-3_62

[15] Hotho A., Staab A., and Stumme G., “Ontologies Improve Text Document Clustering,” in Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, pp. 541- 544, 2003. https://ieeexplore.ieee.org/document/1250972

[16] Jabbar M. and Samreen S., “Heart Disease Prediction System Based on Hidden Naïve Bayes Classifier,” in Proceedings of the International Conference on Circuits, Controls, Communications and Computing (14C), Bangalore, pp. 1-5, 2016. https://ieeexplore.ieee.org/document/8053261

[17] Jeyanthi S., Efficient Classification Algorithms Using SVMs for Large Datasets, Master Theses, Indian Institute of Science, 2007. https://citeseerx.ist.psu.edu/document?repid=rep1 &type=pdf&doi=3f9c48f6cf894d30b9c22d6ced2 c5c77882e4bd6

[18] Kesavaraj G. and Sukumaran S., “A Comparison Study on Performance Analysis of Data Mining Algorithms in Classification of Local Area News Dataset Using WEKA Tool,” International Journal of Engineering Sciences and Research Technology, vol. 2, no. 10, pp. 2748-2755, 2013. https://scholar.google.co.in/citations?user=FXbZ 6e0AAAAJ&hl=en

[19] Kumar N., Kumar V., Kumar G., and Sharma R., “The Role of Human Computer Interaction,” Mukt Shabd Journal, vol. 11, no. 6, pp. 284-287, 2022. https://app.box.com/s/eh96kvxeiov6ek6vjnqhdrb si5cc3vus

[20] Le C. and Shimazu A., “High WSD Accuracy Using Naive Bayesian Classifier with Rich Features,” in Proceedings of the 18th Pacific Asia Conference on Language, Information and Computation, Tokyo, pp. 105-114, 2004. https://aclanthology.org/Y04-1011.pdf

[21] Liu Y., Wang X., Wang L., and Lv Z., “A Bayesian Collocation Method for Static Analysis of Structures with Unknown-But-Bounded Uncertainties,” Computer Methods in Applied Mechanics and Engineering, vol. 346, pp. 727- 745, 2019. https://doi.org/10.1016/j.cma.2018.08.043

[22] Martin-Valdivia M., Urenna-Lopez L., and Garcia-Vega M., “The Learning Vector Quantization Algorithm Applied to Automatic Text Classification Tasks,” Neural Networks, vol. 20, no. 6, pp. 748-756, 2007. https://doi.org/10.1016/j.neunet.2006.12.005

[23] Maurya A. and Bahadur P., Proceedings of Second Doctoral Symposium on Computational Intelligence, Springer, 2021. https://colab.ws/articles/10.1007%2F978-981-16- 3346-1_56

[24] Mitchell T., Machine Learning, McGraw-Hill Science, 1997. https://www.cin.ufpe.br/~cavmj/Machine%20- %20Learning%20-%20Tom%20Mitchell.pdf

[25] Navigli R., “Word Sense Disambiguation: A Survey,” ACM Computing Surveys (CSUR), vol. 41, no. 2, pp. 1-70, 2009. https://dl.acm.org/doi/abs/10.1145/1459352.1459 355

[26] Pal A., Kundu A., Singh A., Shekhar R., and Sinha K., “A Hybrid Approach to Word Sense Disambiguation Combining Supervised and Unsupervised Learning,” International Journal of Artificial Intelligence and Application, vol. 4, no. 4, pp. 89-101, 2013. https://arxiv.org/pdf/1611.01083

[27] Palanati D. and Kolikipogu R., “Decision List Algorithm for Word Sense Disambiguation for Telugu Natural Language Processing,” International Journal of Electronics Communication and Computer Engineering, vol. 4, no. 6, pp. 176-180, 2013. https://ijecce.org/Download/conference/NCRTC ST-2/38NCRTCST-13084.pdf

[28] Pavlyshenko B., “Using Stacking Approaches for Machine Learning Models,” in Proceedings of the 2nd International Conference on Data Stream Mining and Processing, Lviv, pp. 255-258, 2018. https://ieeexplore.ieee.org/document/8478522

[29] Ramachandran R. and Mammone R., Modern Methods of Speech Processing, Springer, 2012. https://www.amazon.in/Processing-Springer- International-Engineering-Computer/dp/1461359627

[30] Ranjan Pal A. and Saha D., “Word Sense Disambiguation: A Survey,” International Journal of Control Theory and Computer Modeling, vol. 5, no. 3, pp. 1-16, 2015. https://www.airccse.org/journal/ijctcm/papers/53 15ijctcm01.pdf

[31] Samuel A., “Some Studies in Machine Learning Using the Game of Checkers,” IBM Journal of Research and Development, vol. 44, no. 3, pp. 535-554, 1959. https://people.csail.mit.edu/brooks/idocs/Samuel.pdf

[32] Settouti N., El Amine Bechar M., and Amine Chikh M., “Statistical Comparisons of the Top 10 Algorithms, in Data Mining for Classification Improvement of Translation Accuracy for the Word Sense Disambiguation System ... 1141 Task,” International Journal of Interactive Multimedia and Artificial Intelligence, vol.4 , no. 1, pp. 46-51, 2016. DOI:10.9781/ijimai.2016.419

[33] Singh R., Ghosh K., Nongmeikapam K., and Bandyopadhyay S., “A Decision Tree Based Word Sense Disambiguation System in Manipuri Language,” Advanced Computing: An International Journal, vol. 5, no. 4, pp. 17-22, 2014. DOI:10.5121/acij.2014.5403

[34] Sinha R. and Jain A., “AnglaHindi: An English to Hindi Machine-Aided Translation System,” in Proceedings of 9th Machine Translation Summit: System Presentations, New Orleans, pp. 1-5, 2003. https://aclanthology.org/2003.mtsummit- systems.15

[35] Szepesvari C., Synthesis Lectures on Artificial Intelligence and Machine Learning, Springer, 2010. https://link.springer.com/book/10.1007/978-3- 031-01551-9

[36] Tiwary U. and Siddiqui T., Natural Language Processing and Information Retrieval, Oxford University Press, 2008. https://dl.acm.org/doi/book/10.5555/1481140

[37] Venkatesan N. and Priya G., “A Study of Random Forest Algorithm with Implementation Using WEKA,” International Journal of Innovative Research in Computer Science and Engineering, vol. 1, no. 6, pp. 156-162, 2015. https://www.ioirp.com/Doc/IJIRCSE/i6/JCSE242.pdf

[38] Verma S. and Chandran S., “Contactless Palmprint Verification System Using 2-D Gabor Filter and Principal Component Analysis,” The International Arab Journal of Information Technology, vol. 16, no. 1, pp. 23-29, 2019. https://ccis2k.org/iajit/PDF/January%202019,%2 0No.%201/11209.pdf

[39] Wang L., “Research and Implementation of Machine Learning Classifier Based on KNN,” IOP Conference Series: Materials Science and Engineering, vol. 677, no. 5, pp. 1-6, 2019. DOI:10.1088/1757-899X/677/5/052038

[40] Wang S. and Wang S., Interdisciplinary Computing in Java Programming, Spriger, 2003. https://link.springer.com/chapter/10.1007/978-1- 4615-0377-4_5

[41] Zenko B., Todorovski L., and Dzeroski S., “A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods,” in Proceedings of the IEEE International Conference on Data Mining, San Jose, pp. 669-670, 2001. https://ieeexplore.ieee.org/document/989601

[42] Zhang Z., “Introduction to Machine Learning: K- Nearest Neighbors,” Annals of Translational Medicine, vol. 4, no. 11, pp. 1-7, 2016. https://pmc.ncbi.nlm.nih.gov/articles/PMC49163 48/