Abstract
At present, the existing methods of English article flip calibration neglect to extract English semantic features, which leads to errors in English flip results and has a great impact on the accuracy and time consumption of translation sentence calibration. Therefore, a semantic feature-based automatic text flipping calibration algorithm is proposed. According to the features of semantic information in machine translation, a semantic grammar tree is constructed to complete the machine turning of English articles. The CART decision tree attribute is obtained, and the random forest method is introduced to extract the input matrix and output matrix of the corpus feature as samples to determine the spatial attribute feature of the mistranslated sentences. Choose 10000 English sentences about human body parts as the experimental object and design the simulation experiment. The experimental results show that the minimum and maximum accuracy rates are 95.4% and 100.0%, respectively. The proposed algorithm is time-consuming, and the KSMR value is lower than that of the traditional method. It is proved that the error rate of English article flipping is significantly reduced.
Highlights
With the development of Internet technology, a lot of English translation software has been born
We propose a new UNMT architecture with a cross-language presentation protocol to capture the interaction between UBWE/CMLM
According to the machine translation features of semantic information [7], a semantic syntax tree [8] is constructed to realize machine translation of English articles. e specific steps are as follows: Step 1: list semantic units according to semantic information and get semantic syntax patterns based on semantic syntax tree; Step 2: align words
Summary
With the development of Internet technology, a lot of English translation software has been born. According to its own algorithm, the English translation software provides the English translation results by searching the semantic vocabulary of the whole web. These English translation results are unreliable and cannot be used directly, and a lot of manual proofreading is needed later. E tree lexical-semantic database of Chinese-English translation is established by using the machine learning method, and the structure is automatically adjusted according to the semantic modification objectives in the tree lexical-semantic database, so as to realize the automatic calibration of Chinese-English translation and subject word registration, calculate the optimal semantic correlation feature quantity of each clause, and use the machine learning algorithm for automatic optimization to realize the automatic calibration of Chinese-English translation.
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