S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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

Guoxi Chai, Qiaozhi Wen, and Fuxing Su

(Received December 31, 2020; Accepted April 15, 2021)

Keywords: linguistic quantitative characteristics, corpus, machine learning, translation style, human translation, online translation

Research on the metrological characteristics of linguistic quantitative characteristics (LQC) 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.

Corresponding author: Fuxing Su




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