![]() ![]() For this disadvantage, some researchers have proposed methods of word extension, such as using dictionaries Word Net、How-Net for word extension. The English words "like" and "love", for example, they all mean like, but in the vector space model, they are treated as two separate lexical items. Although the word bag model method is simple and effective, this method ignores the semantic information of the word in the document and does not take into account the semantic similarity between words. The similarity of the texts is calculated by calculating the cosine value between the vectors. This method manifests the weight of the word by the frequency of the word appearing in the document and the frequency of the word appearing in the document collection. ![]() At present, the most commonly used and classic text representation model is the vector space model, and the TF-IDF algorithm based on the vector space model is the most widely used method to calculate the text similarity. Its core content is to calculate the similarity between texts. Off-topic detection is used to judge whether a composition is off-topic. The most important thing in a composition is that the subject is clear, otherwise it is easy to cause confusion and misunderstanding, or even off-topic. 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.Ĭomposition is an important means to express emotion and transmit information, while the theme is the soul of composition. Different F values are obtained by changing the number of topics in the document, and the best number of topics is determined. 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. 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.
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