Feature Extraction Algorithm in Intelligent Translation Proofreading System

With the rapid development of artificial intelligence technology, intelligent translation proofreading system has been developed. Feature extraction algorithm is one of the key technologies in proofreading system. Feature extraction algorithms are used to extract useful information from the raw data in order to better understand and process the translation results. In order to improve the accuracy and efficiency of translation, this paper studies the feature extraction algorithm in the intelligent translation proofreading system. In this paper, the performance of different feature extraction algorithms is studied by experimental comparison method. In the experiment, a variety of different types of feature extraction algorithms were used, including the word bag model, TF-IDF (term frequency–inverse document frequency), Word 2 Vec (Word 2 vecor) and GloVe (Global Vectors). This paper collects a lot of translation data, and uses different algorithms to process and analyze these data. Finally, the performance of different algorithms is compared. In the experimental test of this paper, the accuracy of proofreading increased to 98% with GloVe reference, indicating that this method has great advantages in Chinese-English translation.