Off-topic Detection Model based on Biterm-LDA and Doc2vec

Liu, Pan; Liu, Jie; Ma, Xiaoli; Zhou, Jianshe

doi:10.1145/3341069.3342989

Cited by 3 publications

(1 citation statement)

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“…Experimental data show that the interdependent representation of short-text pairs is effective and efficient for semantic text similarity tasks. Literature [8] uses the Biterm-LDA model to extract the subject words of the title and the article and combines it with Doc2vec to check the combined subject and semantics. Secondly, the author proposes a threshold calculation method based on the center of the topic composition under different topic composition.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

2021

Mobile Information Systems

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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.

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Section: Introductionmentioning

confidence: 99%