logo
calendar22 Avgust 2023
view30
Main language:Uzbek

DETECTING HOMONIMY BY MEANS OF THE NAIVE BAYES CLASSIFIER

Field of Science:
pdf

65e055465c698.pdf

PDF

ARTICLE ANNOTATION

quote
One of the relevant issues of a natural language processing is word sense disambiguation. Homonyms are considered as an important element of determining the meaning of a word. Methods based on machine learning play a special role in solving this problem. Naive Bayes classifier is one of the important machine learning methods. When eliminating homonymy between different and grammatically similar groups of words in the Uzbek language, the Naive Bayes classifier differs from other methods in its simplicity and speed. This classifier is one of the most popular multi-class classification algorithms, and depending on the data in question, any of the 3 types of Naive Bayes algorithms (Gaussian, Polynomial, Bernoulli) can be used. This article scrutinizes the processes of using the classifier to identify homonymy between grammatically similar groups of words in the Uzbek language.

AUTHORS

B.Elov

Alisher Navoiy nomidagi Toshkent davlat o‘zbek tili va adabiyoti universiteti

X.Axmedova

Alisher Navoiy nomidagi Toshkent davlat o‘zbek tili va adabiyoti universiteti

Tags

# омонимия# homonymy# смысл слова# Natural language processing# tabiiy tilni qayta ishlash jaray# so‘z ma’nosini aniqlash# omonimiya# Naive Bayes klassifikatori# matnlarni tasniflash# aprior va aposterior ehtimollikl# Scikit learn kutubxonasi# обработка естественного языка# наивный байесовский классификато# классификация текстов# априорные и апостериорные вероят# обучающая библиотека Scikit# Word sense disambiguation# Naive Bayes classifier# text classification# prior and posterior probabilitie# Scikit learning library

OTHER ARTICLES IN THIS JOURNAL

Rate Article

0
0 ratings
5
4
3
2
1

Article Identifiers

References

Anggraeni, M., Syafrullah, M., & Damanik, H. (2019). Literation Hearing Impairment (I-Chat Bot): Natural Language Processing (NLP) and Naïve Bayes Method. Journal of Physics: Conference Series, 1201, 1-7. doi:10.1088/1742-6596/1201/1/012057

Axmedova, X. (2022). Chastotali usul yordamida omonimiyani aniqlash [Determining homonymy using frequency method]. Proceedings of the Republican scientific and practical conference “Prospects of applied uzbek philology”, (pp. 164-170). Tashkent.

Axmedova, X. (2022). Turli so‘z turkumlari orasidagi omonimiyani aniqlovchi matematik modellar [Mathematical models for identifying homonymy between different word groups]. Science and Innovation, 1(7), 393-400. doi:10.5281/zenodo.7238546

Bahri, S., Saputra, R., & Wajhillah, R. (2017). Sentiment analysis based on Natural Language (NLP) with Naïve-Bayes classifier. Proceedings of the National Conference on Social Science & Technology, 1 (1), pp. 176-180. Получено из https://www.researchgate.net

Bako, A., Taylor, H., Wiley, K., Zheng, J., Walter-McCabe, H., Kasthurirathne, S., & Vest, J. (2021). Using natural language processing to classify social work interventions. American Journal of Managed Care, 27(1), E24–E31. doi:10.37765/AJMC.2021.88580

Bogery, R., Babtain, N., Aslam, N., Alkabour, N., Hashim, Y., & Khan, I. (2019). Automatic semantic categorization of news headlines using ensemble machine learning: A comparative study. International Journal of Advanced Computer Science and Applications, 10(11), 689–696. doi:10.14569/ IJACSA.2019.0101190

Chifu, A., & Ionescu, R. (2012). Word sense disambiguation to improve precision for ambiguous queries. Open Computer Science, 2(4), 398-411. doi:10.2478/s13537-012-0032-6

Elov, B., & Axmedova, X. (2022). Determining homonymy using statistical methods. Proceedings of the Second Uzbekistan-Malaysia International Conference “Computational Models and Technologies (HMT 2022)”, (pp. 106). Tashkent.

Elov, B., & Axmedova, X. (2022). Uchta so‘z turkumi doirasidagi omonimiyani farqlovchi biznes jarayonni modellashtirish [Modeling a business process that differentiates homonymy within three sets of words]. Science and Innovative Development(1), 150-162. doi:10.36522/2181- 9637-2022-1-15

Foster, J., & Wagner, J. (2021). Naive Bayes versus BERT: Jupyter notebook assignments for an introductory NLP course. Proceedings of the 5th Workshop on Teaching Natural Language Processing (pp. 112-114). Association for Computational Linguistics (ACL). doi:10.18653/v1/2021. teachingnlp-1.20

Granik, M., & Mesyura, V. (2017). Fake news detection using naive Bayes classifier. Proceedings of the 2017 IEEE 1st Ukraine Conference on Electrical and Computer Engineering (UKRCON 2017) (pp. 900-903). Institute of Electrical and Electronics Engineers Inc. doi:10.1109/ UKRCON.2017.8100379

Kaur, C. (2020). Sentiment Analysis of Tweets on Social Issues using Machine Learning Approach. International Journal of Advanced Trends in Computer Science and Engineering, 9(4), 6303– 6311. doi:10.30534/ijatcse/2020/310942020

Ku, C., & Leroy, G. (2014). A decision support system: Automated crime report analysis and classification for e-government. Government Information Quarterly, 31(4), 534-544. doi:10.1016/j. giq.2014.08.003

Kunal, S., Saha, A., Varma, A., & Tiwari, V. (2018). Textual Dissection Of Live Twitter Reviews Using Naive Bayes. Proceedings of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018), 132, pp. 307-313.

Nahar, K., Jaradat, A., Atoum, M., & Ibrahim, F. (2020). Sentiment analysis and classification of arab jordanian facebook comments for jordanian telecom companies using lexicon-based approach and machine learning. Jordanian Journal of Computers and Information Technology, 6(3), 247-262. doi:10.5455/jjcit.71-1586289399

Pal, A., Saha, D., Naskar, S., & Dash, N. (2021). In search of a suitable method for disambiguation of word senses in Bengali. International Journal of Speech Technology, 24(2), 439-454. doi:10.1007/ s10772-020-09787-8

Putong, M., & Suharjito. (2020). Classification model of contact center customers emails using machine learning. Advances in Science, Technology and Engineering Systems, 5(1), 174-182. doi:10.25046/aj050123

Rusli, N., Amir, A., Zahri, N., & Ahmad, R. (2019). Snake species identification by using natural language processing. Indonesian Journal of Electrical Engineering and Computer Science, 13(3), 999–1006. doi:10.11591/ijeecs.v13.i3.pp999-1006

Siddiqui, S., Rehman, M., Daudpota, S., & Waqas, A. (2019). Opinion mining: An approach to feature engineering. International Journal of Advanced Computer Science and Applications, 10(3), 159–165. doi:10.14569/IJACSA.2019.0100320

Taheri, S., & Mammadov, M. (2013). Learning the naive bayes classifier with optimization models. International Journal of Applied Mathematics and Computer Science, 23(4), 787-795. doi:10.2478/amcs-2013-0059