The consistency study of fusion clustering algorithm and improved particle swarm algorithm
In view of the sentence pattern difference between Chinese and English in the process of machine translation,the study measures the sentence consistency by constructing the fitness function.At the same time,improved particle swarm algorithm(Im-proved Particle Swarm Optimization,IPSO)and improved clustering algorithm(Improved K-means algorithm,IK-Means)are intro-duced to solve the fitness function to improve the consistency of Chinese-English sentence translation.The results show that the IPSO algorithm achieves significantly better BLEU scores than other algorithms on different datasets.In the Chinese-English comparable corpus,the bilingual evaluation study(Bilingual Evaluation Understudy,BLEU)reached the highest score of 23.11 and the average value of 21.28,which improved by 8.97 and 11.28 over the basic PSO algorithm.At the same time,on the parallel corpora of Eng-lish and Chinese in China,the BLEU score of IPSO algorithm reached 20.81 and the average value was 18.79.It shows that the ma-chine translation method integrating IK-Means algorithm and IPSO algorithm can have significant translation performance,improve the sentence consistency of Chinese-English translation,and provide a reliable method reference for the improvement of machine translation quality.
万方数据
维普
