Research on Translation Style in Machine Learning Based on Linguistic Quantitative Characteristics Perception

摘要

Research on the metrological characteristics of linguistic quantitative characteristics (LQCs) based on corpus and metrological linguistic methods has gained wide attention in artificial and online machine translations. Although a support vector machine (SVM) is one of the most widely used machine learning (ML) algorithms in the field of text analysis, its application in the study of translation style is rare. This study compares the translation styles of Pride and Prejudice with ML using different linguistic measurement features. Firstly, the language measurement features of three translations are obtained with the information gain algorithm. Specifically, the corpus can be achieved through human-machine interaction (HCI), i.e., computers can look, hear, touch, smell, taste, and speak using sensors such as cameras and mathematical algorithms. Then a text classifier, i.e., an SVM, is constructed on the basis of these features to automatically classify the translated texts of the three translations. Finally, the validity of the classifier is verified by the tenfold cross-validation method. It is proved that the SVM algorithm has high classification accuracy and a strong predictive function, which is helpful for judging or predicting the translation or translator's style. Compared with the traditional method, this classification method based on an SVM saves time and effort, the process can be repeated, and the result is accurate and reliable.