Simulation of English translation text filtering based on machine learning and embedded system

摘要

The translation is generally assumed to distinguish it from the original language text in the same area. Therefore, say these constitute a unique multilingual translation, commonly referred to as translation; translation is also affected by the source language and exhibits different characteristics according to the source language. Therefore, claim that these variants constitute the same target language translated into additional "dialog." Using Machine Learning English and embedded technologies investigated the differences between the general characteristics of different translation sources and language translation. There is little research corpus between the complicated relationship between translation and the original text and translation itself for very different language types. May does not translate enough knowledge of the subject areas covered. There are many guidelines to help authors clearly express their ideas to promote translation; scientists and engineers find it difficult to apply the procedures required for a high degree of speech recognition. For financial reasons, non-essential text may be edited publication. In this case, as described in the article, the author can take some precautions to express terms of intelligibility. Machine Learning and embedded systems determine the importance of various features for data collection and the importance of its characteristics compared with previously reported studies in English. Also, the method allows us to add the grammatical function of translation studies rarely used. The results show that, even if only based on five features, the full translation can be reliably separated from the non-translated region.