Design of Intelligent English Writing Self-evaluation Auxiliary System

Abstract

Since the reform and opening up, the exchanges between China and the world have become more and more frequent. English, as a widely used international language, plays an important role in international exchanges. English teaching includes five aspects, listening, speaking, reading, writing and translation. Writing teaching is very important but difficult. In order to improve students' autonomous writing ability, this paper briefly introduced the real-time multi-writing teaching mode and designed an automatic scoring algorithm of writing self-evaluation auxiliary system, random sampling based Bayesian classification and combinational algorithm. One thousand CET-4 and CET-6 compositions from Chinese Learner English Corpus (CLEC) were evaluated, and the scoring effect of Bayesian classification algorithm was also tested. The results showed that the accuracy rate, recall rate and F value of the proposed algorithm was better than that of Bayesian classification algorithm under 150 feature extraction dimensions, the two algorithms had improved scoring effect under the optimal feature extraction dimensions, and the improvement amplitude of the algorithm proposed in this study was larger. In summary, the random sampling based Bayesian classification and combinational algorithm is effective and feasible as an automatic scoring algorithm of writing self-evaluation auxiliary system.