TY - GEN
T1 - Plagiarism Detection in Students’ Answers Using FP-Growth Algorithm
AU - Nurlybayeva, Sabina
AU - Akhmetov, Iskander
AU - Gelbukh, Alexander
AU - Mussabayev, Rustam
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - According to statistics, over the past year, the quality of education has fallen due to the pandemic, and the percentage of plagiarism in the work of students has increased. Modern plagiarism detection systems work well with external plagiarism, they allow to weed out works and answers that completely copy someone else’s published ideas. Using natural language processing methods, the proposed algorithm allows not only detecting plagiarism, but also correctly classifies students’ responses by the amount of plagiarism. This research paper implements a two-step plagiarism detection algorithm. In the experiment, the text was converted into a vector form by the GloVe method, and then segmented by K-means and the result was obtained by the FP-Growth unsupervised learning algorithm.
AB - According to statistics, over the past year, the quality of education has fallen due to the pandemic, and the percentage of plagiarism in the work of students has increased. Modern plagiarism detection systems work well with external plagiarism, they allow to weed out works and answers that completely copy someone else’s published ideas. Using natural language processing methods, the proposed algorithm allows not only detecting plagiarism, but also correctly classifies students’ responses by the amount of plagiarism. This research paper implements a two-step plagiarism detection algorithm. In the experiment, the text was converted into a vector form by the GloVe method, and then segmented by K-means and the result was obtained by the FP-Growth unsupervised learning algorithm.
KW - Machine learning
KW - Natural language processing
KW - Plagiarism detection
UR - http://www.scopus.com/inward/record.url?scp=85118334282&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-89820-5_12
DO - 10.1007/978-3-030-89820-5_12
M3 - Contribución a la conferencia
AN - SCOPUS:85118334282
SN - 9783030898199
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 153
EP - 162
BT - Advances in Soft Computing - 20th Mexican International Conference on Artificial Intelligence, MICAI 2021, Proceedings
A2 - Batyrshin, Ildar
A2 - Gelbukh, Alexander
A2 - Sidorov, Grigori
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th Mexican International Conference on Artificial Intelligence, MICAI 2021
Y2 - 25 October 2021 through 30 October 2021
ER -