Распознавание смысловой нагрузки текста и конструирование нового текста на основе заданной темы
Аннотация
In this scientific report by Evgeny Zimin, the focus is on the recognition of the semantic load of text and the construction of new text based on a given topic. The report outlines the significance of this task in the realm of natural language processing (NLP) and artificial intelligence (AI), emphasizing the importance of automated systems for text analysis due to the exponential growth of textual data in digital formats.
The primary goal of the research is to develop NLP methods that can efficiently recognize the semantic load of text and generate new text with minimal computational resources. This is crucial for practical applications, especially on devices with limited computational capabilities, such as mobile devices.
The study involves a thorough review of current NLP methods and models, including traditional approaches (N-grams, recurrent neural networks) and advanced models (transformers, BERT, GPT). The report proposes a new hybrid algorithm that combines the strengths of these methods to achieve more accurate and efficient text analysis and generation.
Scientific novelty is highlighted through the development of new algorithms and architectures that deliver high performance while minimizing computational resource consumption. Practical applications are vast, ranging from business analytics, where companies can analyze customer feedback and social media data, to healthcare, where NLP can aid in analyzing medical records and scientific publications.
The report demonstrates that the developed model, tested on real text data, significantly improves the functionality of virtual assistants by ensuring more accurate understanding of user queries and generating relevant responses. This practical significance is further illustrated by successful applications in pilot projects, such as the U.S. Department of the Interior and Canada Parks, and in interactive systems like robots in MSG Sphere, Las Vegas.
The interdisciplinary nature of the research and its wide applicability underscore its relevance and the need for continued investigation in this field, promising advancements in various sectors including education, business, and human-computer interaction.
The primary goal of the research is to develop NLP methods that can efficiently recognize the semantic load of text and generate new text with minimal computational resources. This is crucial for practical applications, especially on devices with limited computational capabilities, such as mobile devices.
The study involves a thorough review of current NLP methods and models, including traditional approaches (N-grams, recurrent neural networks) and advanced models (transformers, BERT, GPT). The report proposes a new hybrid algorithm that combines the strengths of these methods to achieve more accurate and efficient text analysis and generation.
Scientific novelty is highlighted through the development of new algorithms and architectures that deliver high performance while minimizing computational resource consumption. Practical applications are vast, ranging from business analytics, where companies can analyze customer feedback and social media data, to healthcare, where NLP can aid in analyzing medical records and scientific publications.
The report demonstrates that the developed model, tested on real text data, significantly improves the functionality of virtual assistants by ensuring more accurate understanding of user queries and generating relevant responses. This practical significance is further illustrated by successful applications in pilot projects, such as the U.S. Department of the Interior and Canada Parks, and in interactive systems like robots in MSG Sphere, Las Vegas.
The interdisciplinary nature of the research and its wide applicability underscore its relevance and the need for continued investigation in this field, promising advancements in various sectors including education, business, and human-computer interaction.