Open AccessApplication of LDA and word2vec to detect English off-topic composition
Author(s) -
Yilan Qi,
Jun He
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0264552
Subject(s) - word2vec , computer science , composition (language) , topic model , feature vector , artificial intelligence , feature (linguistics) , space (punctuation) , natural language processing , vector space model , information retrieval , pattern recognition (psychology) , philosophy , linguistics , embedding , operating system
This paper presents an off-topic detection algorithm combining LDA and word2vec aiming at the problem of the lack of accurate and efficient off-topic detection algorithms in the English composition-assisted review system. The algorithm uses the LDA model to model the document and train the document through the word2vec, and uses the semantic relationship between the document’s topics and words to calculate the probability weighted sum for each topic and its feature words in the document, and finally selects the off-topic composition by setting a reasonable threshold. Different F values are obtained by changing the number of topics in the document, and the best number of topics is determined. Experimental results show that the proposed method is more effective than vector space model, can detect more off-topic compositions, and the accuracy is higher, the F value is more than 88%, which realizes the intelligent processing of off-topic detection of composition, and can be effectively applied in English composition teaching.