The automatic processing of the texts in natural language. Some bibliometric indicators of the current state of this research area. / Barakhnin, V. B.; Duisenbayeva, A. N.; Kozhemyakina, O. Yu et al.
In: Journal of Physics: Conference Series, Vol. 1117, No. 1, 012001, 27.11.2018.Research output: Contribution to journal › Conference article › peer-review
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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 -
ID: 25326345