NATURAL LANGUAGE PROCESSING AND ITS MAIN APPROACHES

Authors

  • Suyunova Mohinur Author

Keywords:

Statistical approaches, classical approaches, deep learning approaches, intelligent language tutoring systems, NLP algorithms, mal-rule technique, meta-rule.

Abstract

The article explores the relevance of NLP (or Computational Linguistics) to language learning, namely written and spoken communication. In addition, it further focuses on characterizing the techniques of NLP and uses of it for language learning. Together with presenting the background necessary to explain main principles of NLP, the paper also investigates the use of NLP in detecting language faults by students when they are learning the language as a second language.

References

Chinkina, M., & Meurers, D. Linguistically aware information retrieval: Providing input enrichment for second language learners. In Proceedings of the 11thWorkshop on Innovative Use of NLP for Building Educational Applications (BEA) (pp. 188–98). San Diego, CA: Association for Computational Linguistics. Retrieved April 4, 2019 from https://www.semanticscholar.org/paper/Linguistically-Aware-Information- Retrieval%3A-Input-Chinkina-Meurers/d1e6e66b181b5912c101a19d345e56a0c5c28bba. ,2016.

Chollampatt, S., & Ng, H. T. A multilayer convolutional encoder–decoder neural network for grammatical error correction. In Thirty-Second AAAI Conference on Arti!cial Intelligence (AAAI) (pp. 5755–62). New Orleans, LA: AAAI, 2018.

DuBay, W. H. The principles of readability. CostaMesa, CA: Impact Information. Retrieved April 4, 2019 from http://www.impact-information.com/impactinfo/readability02.pdf, 2004.

Abdurakhmonova, N., Alisher, I., & Toirova, G. (2022, September). Applying Web Crawler Technologies for Compiling Parallel Corpora as one Stage of Natural Language Processing. In 2022 7th International Conference on Computer Science and Engineering

(UBMK) (pp. 73-75). IEEE.

Абдурахмонова, Н., & Бойсариева, С. (2023). TABIIY TILNI QAYTA ISHLASHDA (NLP) OKKAZIONALIZMLARNING MORFEM TAHLILI. МЕЖДУНАРОДНЫЙ ЖУРНАЛ ИСКУССТВО СЛОВА, 6(3).

Abdurakhmonova, N. Z., Ismailov, A. S., & Mengliev, D. (2022, November). Developing NLP Tool for Linguistic Analysis of Turkic Languages. In 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) (pp. 1790-1793). IEEE.

Mengliev, D. B., Abdurakhmonova, N., Hayitbayeva, D., & Barakhnin, V. B. (2023, November). Automating the transition from dialectal to literary forms in Uzbek language texts: an algorithmic perspective. In 2023 IEEE XVI International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering (APEIE) (pp. 1440-1443). IEEE.

San’atbek, M., Mirsaid, A., & Nilufar, A. (2018). Modeling WordNet type thesaurus forUzbek language semantic dictionary. International Journal of Systems Engineering, 2(1),26.

Foth, K., Menzel, W., & Schröder. Robust parsing with weighted constraints. Natural Language Engineering, 11(1), 1–25, 2005.

Johnson,M. Two ways of formalizing grammars. Linguistics and Philosophy, 17(3), 221– 48, 1994.

Lightbown, P. M., & Spada, N. How languages are learned. Oxford, England: Oxford University Press, 1999.

Reuer,V. Error recognition and feedback with lexical functional grammar. CALICO Journal, 20(3), 497–512, 2003.

Schmidt, R. Consciousness and foreign language learning: A tutorial on the role of attention and awareness in learning. In R. Schmidt (Ed.), Attention and awareness in foreign language learning (pp. 1–63). Honolulu: University of Hawai‘i Press, 1995.

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Published

2024-06-24

Issue

Section

SECTION 3. Language and speech analysis in NLP (morphological, syntactic and semantic analysis; speech analysis and synthesis).

How to Cite

NATURAL LANGUAGE PROCESSING AND ITS MAIN APPROACHES. (2024). «CONTEMPORARY TECHNOLOGIES OF COMPUTATIONAL LINGUISTICS», 2(22.04), 304-309. https://myscience.uz/index.php/linguistics/article/view/68