Natural Language Processing and Deep Learning for Blended Learning as an Aspect of Computational Linguistics

被引:0
|
作者
Pikhart, Marcel [1 ]
机构
[1] Univ Hradec Kralove, Fac Informat & Management, Hradec Kralove, Czech Republic
来源
CROSS REALITY AND DATA SCIENCE IN ENGINEERING | 2021年 / 1231卷
关键词
Blended learning; E-learning; Computational linguistics; Corpus linguistics; Natural language processing; Deep learning; MOBILE DEVICES; IMPACT; COMMUNICATION; PERFORMANCE;
D O I
10.1007/978-3-030-52575-0_69
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Machine learning has been used for several years already in information science to create algorithms which enable computers to solve problems without giving them particular tasks and instructions to perform these tasks. This paper attempts to concentrate on possibilities of this AI (artificial intelligence) subfield inasmuch it could prove helpful for blended learning. It brings possible questions which are connected to e-learning, blended learning and machine learning and its utilization in university courses. It also brings several questions of computational linguistics which could prove extremely helpful in blended learning processes in which it could bring big data analysis. These phenomena are relatively new, therefore, neglected by blended learning scholars, thus this paper brings these new ideas together and wants to present new ideas and concepts which will be utilized in blended learning area. It also suggests new approach to blended learning, coined by the term blended learning 2.0, which implements modern approaches such as computational linguistics and corpus linguistics into the utilization of e-platforms in the educational process.
引用
收藏
页码:842 / 848
页数:7
相关论文
共 50 条
  • [11] Inflectional Review of Deep Learning on Natural Language Processing
    Fahad, S. K. Ahammad
    Yahya, Abdulsamad Ebrahim
    2018 INTERNATIONAL CONFERENCE ON SMART COMPUTING AND ELECTRONIC ENTERPRISE (ICSCEE), 2018,
  • [12] Are Deep Learning Approaches Suitable for Natural Language Processing?
    Alshahrani, S.
    Kapetanios, E.
    NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS, NLDB 2016, 2016, 9612 : 343 - 349
  • [13] A Deep Learning Architecture for Psychometric Natural Language Processing
    Ahmad, Faizan
    Abbasi, Ahmed
    Li, Jingjing
    Dobolyi, David G.
    Netemeyer, Richard G.
    Clifford, Gari D.
    Chen, Hsinchun
    ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2020, 38 (01)
  • [14] Understanding customer satisfaction via deep learning and natural language processing
    Aldunate, Angeles
    Maldonado, Sebastian
    Vairetti, Carla
    Armelini, Guillermo
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 209
  • [15] Deep Learning for Natural Language Processing and Language Modelling
    Klosowski, Piotr
    2018 SIGNAL PROCESSING: ALGORITHMS, ARCHITECTURES, ARRANGEMENTS, AND APPLICATIONS (SPA), 2018, : 223 - 228
  • [16] Deep Learning Methods in Natural Language Processing
    Flores, Alexis Stalin Alulema
    APPLIED TECHNOLOGIES (ICAT 2019), PT II, 2020, 1194 : 92 - 107
  • [17] Deep learning in clinical natural language processing: a methodical review
    Wu, Stephen
    Roberts, Kirk
    Datta, Surabhi
    Du, Jingcheng
    Ji, Zongcheng
    Si, Yuqi
    Soni, Sarvesh
    Wang, Qiong
    Wei, Qiang
    Xiang, Yang
    Zhao, Bo
    Xu, Hua
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2020, 27 (03) : 457 - 470
  • [18] ENHANCING NATURAL LANGUAGE PROCESSING USING WALRUS OPTIMIZER WITH SELF-ATTENTION DEEP LEARNING MODEL IN APPLIED LINGUISTICS
    Hassan, Abdulkhaleq Q. A.
    Alshammari, Alya
    Zaqaibeh, Belal
    Alzaidi, Muhammad Swaileh a.
    Allafi, Randa
    Alazwari, Sana
    Aljabri, Jawhara
    Nouri, Amal M.
    FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY, 2024,
  • [19] Algorithmic trading strategy based on the integration of deep learning models and natural language processing
    Sadeghi, Nesa
    Kianfar, Kamran
    Doust, Nasser Ghaem
    Fooladi, Jaber
    INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, 2024,
  • [20] Analysis of news sentiments using natural language processing and deep learning
    Vicari, Mattia
    Gaspari, Mauro
    AI & SOCIETY, 2021, 36 (03) : 931 - 937