Assessment of the Dynamics of Publication Activity in the Field of Natural Language Processing and Deep Learning

Natural language processing (NLP) is a rapidly developing field of research. In solving the problems of NLP, along with traditional methods based on a statistical model of language, machine learning methods (ML) are used. The paper considers bibliometric indicators NLP and ML. Dynamic indicators are evaluated and areas of research with the highest growth rates are identified. The indicators were calculated for the following NLP applications: Grammar Checking, Information Extraction, Text Categorization, Dialog Systems, Speech Recognition, Machine Translation, Information Retrieval, Question Answering, Opinion Mining, Smart advisors, etc. The greatest values of dynamic indicators are demonstrated by: Grammar Checking, Information Extraction, Machine Translation, Question Answering. At the same time, the following methods of solving NLP problems are developing most dynamically: Machine Learning, Deep Learning, Neural Networks and Supervised Learning. In turn, Deep Learning is used to solve a wide range of applications. Its feature is the increased requirements for the volume of data processed. The paper assesses the growth dynamics of bibliometric indicators for some Deep Learning applications. The most dynamically developing research is the field of deep learning applications to solve healthcare problems.