MULTI-WORD LEXEME PROCESSING IN GLOBAL COMPUTATIONAL LINGUISTICS

The article reviews current trends in multi-word lexeme processing in computational linguistics, emphasizing the importance of effective natural language analysis. Key areas include: Deep learning: Using neural networks to automatically extract features from texts and work with large amounts of data. Semantic models: Using word vector representations to improve the accuracy of analysis and understand semantic relationships. Multilingual systems: Developing technologies that provide high-quality analysis and translation of texts in different languages. Big data processing: Creating methods for fast and efficient processing of arrays of text information. Integration with other areas: Combining computational linguistics technologies with image and sound processing for complex analysis. The article also highlights the contributions of renowned researchers such as Noam Chomsky and Karen Spark Johnson to the development of this field. Particular attention is paid to the benefits of multi-word lexeme processing for Turkic languages, including taking into account morphological complexity, resolving ambiguities, and improving the quality of machine translation. The work emphasizes the importance of this area for the development of language technologies and communication in the Turkic language community.