This work reviews the bibliometric indicators of a rapidly developing field of research as automatic text processing (Natural language processing). The differential indicators of speed and acceleration were used to evaluate the development dynamics of NLP domains. The evaluation was based on the data from the Science direct bibliometric database. The evaluation of the Russian research segment was conducted according to e-library data. The calculations for the following subdomains of NLP were performed: Grammar Checking, Information Extraction, Text Categorization, Dialog Systems, Speech Recognition, Machine Translation, Information Retrieval, Question Answering, Opinion Mining, Smart advisors and others. The areas with high growth rates (Grammar Checking, Information Extraction, Machine Translation and Question Answering) and the areas that have lost the previously existing dynamics of growth of the publication activity (Information Retrieval, Opinion Mining, Text Categorization) have been identified.
|Журнал||Journal of Physics: Conference Series|
|Состояние||Опубликовано - 27 ноя 2018|
|Опубликовано для внешнего пользования||Да|
|Событие||2018 3rd Big Data Conference, BDC 2018 - Moscow, Российская Федерация|
Продолжительность: 14 сен 2018 → …