TABIIY TILNI QAYTA IShLAShDA SEMANTIK O‘XShAShLIKNI ANIQLAShNING FORMAL USULLARI

Avtorlar

  • Abdullayev Elmurod Zaylobidinovich

    Andijan State University image/svg+xml

Gilt sózler: tabiiy tilni qayta ishlash, semantik o‘xshashlik, formal model, vektor fazosi, algoritmik tahlil

Annotaciya

Maqolada tabiiy tilni qayta ishlash jarayonida semantik o‘xshashlikni aniqlash masalasi nazariy va formal yondashuvlar asosida tahlil qilinadi. Semantik o‘xshashlik tushunchasi matematik va algoritmik modellar orqali aniqlanib, vektor fazosiga asoslangan, ehtimolli hamda grafik modellarning nazariy asoslari yoritiladi. Tadqiqotda semantik o‘xshashlikni baholashning formal mezonlari ishlab chiqilib, ularning umumlashtirish qobiliyati va hisoblash xossalari bilan bog‘liqligi asoslab beriladi. Olingan natijalar NLP algoritmlarini nazariy baholash va takomillashtirish uchun ilmiy asos bo‘lib xizmat qiladi.

Paydalanılǵan ádebiyatlar

1. Salton G. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Reading (MA): Addison-Wesley, 1989.

2. Jurafsky D., Martin J.H. Speech and Language Processing. 3rd ed. – Pearson Education, 2023.

3. Mikolov T., Chen K., Corrado G., Dean J. Distributed Representations of Words and Phrases and their Compositionality. // Advances in Neural Information Processing Systems. 2013. Vol. 26. – P. 3111-3119.

4. Goldberg Y. Neural Network Methods for Natural Language Processing. – San Rafael (CA): – Morgan & Claypool Publishers, 2017.

5. Fellbaum C. (Ed.) WordNet: An Electronic Lexical Database. – Cambridge (MA): MIT Press, 1998.

6. Vapnik V. N. Statistical Learning Theory. – New York: Wiley-Interscience, 1998.

7. Mohri M., Rostamizadeh A., Talwalkar A. Foundations of Machine Learning. 2nd ed. – Cambridge (MA): MIT Press, 2018.

8. Bishop C. M. Pattern Recognition and Machine Learning. – New York: Springer, 2006.

9. Manning C. D., Raghavan P., Schütze H. Introduction to Information Retrieval. – Cambridge: Cambridge University Press, 2008.

10. Cover T. M., Thomas J. A. Elements of Information Theory. 2nd ed. – Hoboken (NJ): Wiley-Interscience, 2006.

11. Bousquet O., Elisseeff A. Stability and Generalization. // Journal of Machine Learning Research. Vol. 2, 2002. – P. 499-526.

Zhang T. Statistical Behavior and Consistency of Classification Methods. // Annals of Statistics. Vol. 32, №1, 2004. – P. 56-134.