Recognition and segmentation of english long and short sentences based on machine translation

Abstract

With the advent of the information age, long sentences which include many words and have more complex structures. The translation of long sentences in English-Chinese machine translation has always been the focus of research. In this study, 400 long sentences were randomly selected from NTCIR-9 patent corpus for testing the recognition and segmentation effects of regular match method and error-driven method, and the accuracy rate of the translation was compared on Baidu Online Translation Platform. The results demonstrated that the regular matching method was effective in recognizing and segmenting long sentences, nevertheless there were many defects; the errordriven method was more effective in recognizing and segmenting long sentences; the former increased by 4.8% of the BLEU value of the translated text on Baidu Online Translation Platform and the latter increased by 12.1%, which showed that the error-driven method was more effective in machine translation.