TY - JOUR

T1 - The automatic processing of the texts in natural language. Some bibliometric indicators of the current state of this research area

AU - Barakhnin, V. B.

AU - Duisenbayeva, A. N.

AU - Kozhemyakina, O. Yu

AU - Yergaliyev, Y. N.

AU - Muhamedyev, R. I.

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2018/11/27

Y1 - 2018/11/27

N2 - 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.

AB - 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.

KW - Natural language processing

KW - Machine Learning

KW - Bibliometric Indicators

KW - Scientometrics

KW - Deep Learning

KW - Neural Networks

KW - Information Extraction

KW - Text Categorization

KW - Dialog Systems

KW - Speech Recognition

KW - Machine Translation

KW - Information Retrieval

KW - Question Answering

KW - Opinion Mining

KW - Smart advisors

KW - D1

KW - D2

KW - semantic network

UR - http://www.scopus.com/inward/record.url?scp=85058330877&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1117/1/012001

DO - 10.1088/1742-6596/1117/1/012001

M3 - Conference article

AN - SCOPUS:85058330877

VL - 1117

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012001

T2 - 2018 3rd Big Data Conference, BDC 2018

Y2 - 14 September 2018

ER -