Automatic generation of questions based on semantic text analysis

被引:0
作者
Ilya, Buldin [1 ]
Vadim, Murov [1 ]
Silnov, Dmitry [1 ]
机构
[1] Natl Res Nucl Univ, MEPhI Moscow Engn Phys Inst, Dept Comp Syst & Technol, Moscow, Russia
来源
2020 IEEE 14TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT2020) | 2020年
关键词
semantics; test generation; pattern; natural language; computer linguistics;
D O I
10.1109/AICT50176.2020.9368686
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Recently, scientists have shown great interest in the processing of natural language. This article presents a template-based approach to the automatic generation of test questions that takes note of the semantic structure of the text. The main idea of the work consists in an automated composition of questions for testing from affirmative sentences that make up the text. The generated questions are then selected by the teacher. The results of experiments with text fragments from literary sources on various topics are given. The main problems for further work and successful testing of the software product in the conditions of classrooms of educational institutions are singled out.
引用
收藏
页数:4
相关论文
共 50 条
[21]   SABATPG - A STRUCTURAL-ANALYSIS BASED AUTOMATIC TEST-GENERATION SYSTEM [J].
LI, ZC ;
PAN, YQ ;
MIN, YH .
SCIENCE IN CHINA SERIES A-MATHEMATICS PHYSICS ASTRONOMY, 1994, 37 (09) :1104-1114
[22]   Automatic theory generation from analyst text files using coherence networks [J].
Shaffer, Steven C. .
NEXT-GENERATION ANALYST II, 2014, 9122
[23]   An Autonomous Transportation System Architecture Mapping Relation Generation Method Based on Text Analysis [J].
Deng, Zhuolin ;
Xiong, Chen ;
Cai, Ming .
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2022, 9 (06) :1768-1776
[24]   SEMANTIC AND AUTOMATIC INDEXING: study of subject analysis of thesis and dissertations [J].
Bruzinga Borges, Graciane Silva ;
Moreira dos Santos Maculan, Benildes Coura ;
Borem de Oliveira Lima, Gercina Angela .
INFORMACAO & SOCIEDADE-ESTUDOS, 2008, 18 (02) :181-193
[25]   When Shallow is Good Enough: Automatic Assessment of Conceptual Text Complexity using Shallow Semantic Features [J].
Stajner, Sanja ;
Hulpus, Ioana .
PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020), 2020, :1414-1422
[26]   Semantic Enrichment of Text Representation with Wikipedia for Text Classification [J].
Yamakawa, Hiroki ;
Peng, Jing ;
Feldman, Anna .
IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010), 2010,
[27]   Text mining using nonnegative matrix factorization and latent semantic analysis [J].
Hassani, Ali ;
Iranmanesh, Amir ;
Mansouri, Najme .
Neural Computing and Applications, 2021, 33 (20) :13745-13766
[28]   Analysis of the Automatic Test Generation Tool: CREST [J].
Chen, Ruidong ;
Luo, Yu ;
Li, Ruixing ;
Zhang, Xiaosong ;
Ying, Lingyun .
2016 INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION, BIG DATA & SMART CITY (ICITBS), 2017, :68-72
[29]   Semantic Sensing Performance Analysis: Assessing Keyword Coverage in Text Data [J].
Yang, Yaoqi ;
Zhang, Bangning ;
Guo, Daoxing ;
Xu, Renhui ;
Wang, Weizheng ;
Xiong, Zehui ;
Niyato, Dusit .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2023, 72 (11) :15133-15137
[30]   A Novel Model of Generative Automatic Text Summarization Based on BART [J].
Wang, Yahui ;
Chang, Qingxia ;
Meng, Xuelei .
IAENG International Journal of Computer Science, 2025, 52 (02) :507-514