Off-Topic Detection of Business English Essay Based on Deep Learning Model

The automatic scoring system of business English essay has been widely used in the field of education, and it is indispensable for the task of off-topic detection of essay. Most of the traditional off-topic detection methods convert text into vector representation of vector space and then calculate...

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Autor principal:	Yiting Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/8e747ad6207f4174b9562d7686f6f8e7
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id	oai:doaj.org-article:8e747ad6207f4174b9562d7686f6f8e7
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spelling	oai:doaj.org-article:8e747ad6207f4174b9562d7686f6f8e72021-11-08T02:36:46ZOff-Topic Detection of Business English Essay Based on Deep Learning Model1875-905X10.1155/2021/5051667https://doaj.org/article/8e747ad6207f4174b9562d7686f6f8e72021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5051667https://doaj.org/toc/1875-905XThe automatic scoring system of business English essay has been widely used in the field of education, and it is indispensable for the task of off-topic detection of essay. Most of the traditional off-topic detection methods convert text into vector representation of vector space and then calculate the similarity between the text and the correct text to get the off-topic result. However, those methods only focus on the structure of the text, but ignore the semantic association. In addition, the traditional detection method has a low off-topic detection effect for essays with high divergence. In view of the above problems, this paper proposes an off-topic detection method for business English essay based on the deep learning model. Firstly, the word2vec model is used to represent words in sentences as word vectors. And, LDA is used to extract the vector of topic and text, respectively. Then, word vector and topic word vector are spliced together as the input of the convolutional neural network (CNN). CNN is used to extract and screen the features of sentences and perform similarity calculation. When the similarity is less than the threshold, the paper also maps the topic and the subject words in the coupling space and calculates their relevance. Finally, unsupervised off-topic detection is realized by the clustering method. The experimental results show that the off-topic detection method based on the deep learning model can improve the detection accuracy of both the essays with low divergence and the essays with high divergence to a certain extent, especially the essays with high divergence.Yiting ZhuHindawi LimitedarticleTelecommunicationTK5101-6720ENMobile Information Systems, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Yiting Zhu Off-Topic Detection of Business English Essay Based on Deep Learning Model
description	The automatic scoring system of business English essay has been widely used in the field of education, and it is indispensable for the task of off-topic detection of essay. Most of the traditional off-topic detection methods convert text into vector representation of vector space and then calculate the similarity between the text and the correct text to get the off-topic result. However, those methods only focus on the structure of the text, but ignore the semantic association. In addition, the traditional detection method has a low off-topic detection effect for essays with high divergence. In view of the above problems, this paper proposes an off-topic detection method for business English essay based on the deep learning model. Firstly, the word2vec model is used to represent words in sentences as word vectors. And, LDA is used to extract the vector of topic and text, respectively. Then, word vector and topic word vector are spliced together as the input of the convolutional neural network (CNN). CNN is used to extract and screen the features of sentences and perform similarity calculation. When the similarity is less than the threshold, the paper also maps the topic and the subject words in the coupling space and calculates their relevance. Finally, unsupervised off-topic detection is realized by the clustering method. The experimental results show that the off-topic detection method based on the deep learning model can improve the detection accuracy of both the essays with low divergence and the essays with high divergence to a certain extent, especially the essays with high divergence.
format	article
author	Yiting Zhu
author_facet	Yiting Zhu
author_sort	Yiting Zhu
title	Off-Topic Detection of Business English Essay Based on Deep Learning Model
title_short	Off-Topic Detection of Business English Essay Based on Deep Learning Model
title_full	Off-Topic Detection of Business English Essay Based on Deep Learning Model
title_fullStr	Off-Topic Detection of Business English Essay Based on Deep Learning Model
title_full_unstemmed	Off-Topic Detection of Business English Essay Based on Deep Learning Model
title_sort	off-topic detection of business english essay based on deep learning model
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/8e747ad6207f4174b9562d7686f6f8e7
work_keys_str_mv	AT yitingzhu offtopicdetectionofbusinessenglishessaybasedondeeplearningmodel
_version_	1718443116498780160

Off-Topic Detection of Business English Essay Based on Deep Learning Model

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