PROBLEM OF SUPPLY IN COMPUTER LINGUISTICS AND LINGUISTIC SUPPLY OF SYNONYMOUS UNITS
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Computer linguistics, a dynamic field at the intersection of linguistics and computer science, plays a crucial role in natural language processing (NLP) applications such as machine translation, sentiment analysis, and speech recognition. One of the fundamental challenges in computer linguistics is the problem of supply, specifically the availability and accessibility of linguistic resources. This article explores the problem of supply in computer linguistics, with a focus on the linguistic supply of synonymous units. We discuss the significance of linguistic resources, the challenges of synonymy, and the potential solutions to bridge the gap between linguistic supply and computational demand.
Nilufar SOTVOLDIYEVA. (2024). PROBLEM OF SUPPLY IN COMPUTER LINGUISTICS AND LINGUISTIC SUPPLY OF SYNONYMOUS UNITS. «ACTA NUUz», 1(1.1.1), 310–313. https://doi.org/10.69617/nuuz.v1i1.1.1.803
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2. Lyons, J. (1977). Semantics. Cambridge University Press.
3. McCarthy, D., & Navigli, R. (2007). Semeval-2007 Task 10: English Lexical Substitution Task. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007) (pp. 48-53).
4. Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
5. Mihalcea, R., & Pedersen, T. (2005). SenseRank: Bringing Order into WordNet Senses. In Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing (pp. 610-617).
6. Faruqui, M., Dyer, C., & Roth, D. (2016). Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 1116-1126).
7. Nivre, J., et al. (2016). Universal Dependencies v1: A Multilingual Treebank Collection. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) (pp. 1659-1666).
8. Snow, R., O'Connor, B., Jurafsky, D., & Ng, A. Y. (2008). Cheap and Fast—but is it Good?: Evaluating Non-Expert Annotations for Natural Language Tasks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 254-263).
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