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Интеллектуальная Система Тематического Исследования НАукометрических данных |
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AI for AI: What NLP techniques help researchers find the right articles on NLPстатья
- Авторы: Sergei Prokhorov, Victor Safronov
- Сборник: Proceedings of 2019 International Conference on Artificial Intelligence: Applications and Innovations
- Редактор: Прохоров Сергей Петрович
- Год издания: 2019
- Место издания: IEEE Computer Society Los Alamitos, United States
- Первая страница: 72
- Последняя страница: 77
- DOI: 10.1109/IC-AIAI.2019.00023
- Аннотация: The human progress of the coming years is largely associated with success in the field of artificial intelligence methods. The growth of knowledge in this area is in the nature of an information explosion. Researchers have to spend too much time monitoring a constantly evolving field, filtering out the flow of complex documents and texts that require a deep understanding of machine learning methods and their application in specific areas. In solving this problem, the very methods of machine learning and natural language processing are highly effective. The ability to take into account complex non-linear dependencies allows modern language models to effectively solve the problems of information retrieval, monitoring, facts extraction and further analysis. The article provides an overview of modern natural language processing methods that form the basis of modern text information retrieval systems and semi-automatic compilation of reviews and roadmaps. A review of approaches of text vectorization methods for semantic classification of documents. Known limitations of these techniques are also discussed.
- Добавил в систему: Прохоров Сергей Петрович