Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Named Entity Extraction Model Based on the Random Walk Method. / Mansurova, Madina; Barakhnin, Vladimir; Kyrgyzbayeva, Marzhan et al.
SIST 2021 - 2021 IEEE International Conference on Smart Information Systems and Technologies. Institute of Electrical and Electronics Engineers Inc., 2021. 9465992 (SIST 2021 - 2021 IEEE International Conference on Smart Information Systems and Technologies).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Named Entity Extraction Model Based on the Random Walk Method
AU - Mansurova, Madina
AU - Barakhnin, Vladimir
AU - Kyrgyzbayeva, Marzhan
AU - Kadyrbek, Nurgali
N1 - Funding Information: VIII. ACKNOWLEDGEMENT This research was funded by grant of Ministry of Education and Science of the Republic of Kazakhstan number AP09261344 "Development of methods for automatic extraction of spatial objects from heterogeneous sources for information support of geographic information systems". Publisher Copyright: © 2021 IEEE.
PY - 2021/4/28
Y1 - 2021/4/28
N2 - In connection with the rapid development of Internet technologies, modern society in recent decades has experienced an information explosion characterized by an exponential increase in the volume of information, including low quality information. This work is intended to provide all interested parties with intelligent tools to support decision-making by automatically extracting knowledge from heterogeneous data sources, including the Internet. In the work, we examined the primary processing and morphological analysis of texts, implemented a random walk method to extract semantically related words. As a result of the calculations, we got a matrix with the affinities of words, as well as a dictionary that connects the word with the vector component. In addition, the neural network, trained to retrieve linguistic constructions, which include the possible values of descriptors of named text entities, was described in the work.
AB - In connection with the rapid development of Internet technologies, modern society in recent decades has experienced an information explosion characterized by an exponential increase in the volume of information, including low quality information. This work is intended to provide all interested parties with intelligent tools to support decision-making by automatically extracting knowledge from heterogeneous data sources, including the Internet. In the work, we examined the primary processing and morphological analysis of texts, implemented a random walk method to extract semantically related words. As a result of the calculations, we got a matrix with the affinities of words, as well as a dictionary that connects the word with the vector component. In addition, the neural network, trained to retrieve linguistic constructions, which include the possible values of descriptors of named text entities, was described in the work.
KW - named entity extraction
KW - neural network
KW - random walk method
UR - http://www.scopus.com/inward/record.url?scp=85113901160&partnerID=8YFLogxK
U2 - 10.1109/SIST50301.2021.9465992
DO - 10.1109/SIST50301.2021.9465992
M3 - Conference contribution
AN - SCOPUS:85113901160
T3 - SIST 2021 - 2021 IEEE International Conference on Smart Information Systems and Technologies
BT - SIST 2021 - 2021 IEEE International Conference on Smart Information Systems and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Smart Information Systems and Technologies, SIST 2021
Y2 - 28 April 2021 through 30 April 2021
ER -
ID: 34152732