基于LDA和word2vec的英文作文跑题检测.

Aiming at the problem that the lack of accurate and efficient off-topic detection algorithm for the current English composition teaching system in China, this paper proposed an off-topic detection algorithm based on LDA and word2vec. The algorithm used LDA to model the documents and trained it with word2vec, with obtained semantic relation between document’s topic and words, calculated the probability weighted sum of each topic and its feature words in the document. Finally, by setting reasonable threshold, it selected the off-topic essays. According to the different F values for the different number of topics in the document, it determined the optimum number of topics in the experiment. The experimental results show that, compared to traditional vector space model, the proposed method can detect more off-topic essays with higher accuracy, and the F value is above 89%, which realizes the intelligent processing of off-topic essays detection, and may applies effectively in English essays teaching. [ABSTRACT FROM AUTHOR]