STUDY OF NATURAL LANGUAGE PROCESSING METHODS FOR CREATING INTELLIGENT SYSTEMS, SUCH AS CHATBOTS AND AUTOMATIC TRANSLATION SYSTEMS

Abstract

The article's purpose is to study NLP methods for creating intelligent systems, such as chatbots and automatic translators, and to improve their efficiency, accuracy, and adaptability to user needs.

Scientific novelty. This article discusses modern approaches to natural language processing, including the use of transducers, neural networks, and deep learning for real-time text processing. Optimizations for parsing, text generation, and integration of multilingual models are introduced to improve the quality of translation and dialog systems.

Results. The main methods of natural language processing, such as tokenization, lemmatization, and stemming, as well as their impact on text pre-processing are analyzed. The advantages of contextual models (BERT, GPT) compared to static vector representations of words are revealed, which allows for more efficient consideration of semantic and syntactic dependencies. The effectiveness of attention mechanisms in converters for processing large datasets and multilingual texts is demonstrated, which improves translation accuracy and dialog quality. It was found that optimizing the architecture of deep neural networks (adding global and local attention mechanisms) significantly improved the chatbot's performance during contextual tasks. This article discusses the use of tone analysis and named entity recognition (NER) methods to improve the chatbot's adaptability to different use cases. Recommendations for integrating natural language processing models into automatic translation systems while minimizing computational costs and maintaining high performance are formulated.

Conclusion The development and implementation of modern natural language processing models can significantly improve the efficiency of systems such as chatbots and automatic translators, providing a more natural interaction with users. The use of contextual models, transducers, and attention mechanisms helps to improve the quality of text processing, especially in multilingual environments. Adaptive model integration can expand chatbots' capabilities, for example, in healthcare, education, and customer-oriented industries. Further developments in natural language processing will focus on developing multimodal systems capable of analyzing text, images, and audio, as well as ensuring transparency of model decisions to increase user trust. Key areas for further research include minimizing the resource consumption of models, improving their ability to work in real-time, and expanding the availability of technology to accommodate different languages and cultures.