| Improvement of English-Chinese Translation Method by Feature Reduction and Rule Optimization Based on Rough Set Theory |
| Hongxia Wei Foreign Languages School of Anhui Polytechnic University Anhui Wuhu 241000 China |
| Abstract: The traditional machine translation algorithm faces low efficiency and low accuracy due to complex grammar and multiple rules of English noun phrases. A kind of noun phrase identification method based on a rough set was proposed to improve the accuracy of English noun phrases. The rough set method regarded the identification of English noun phrases as a decision problem. We used rough set theory to reduce features, optimize rules for English noun phrases, and finally identify them. Then, a simulation experiment was carried out on an English noun phrase sample on the Wall Street Journal (WSJ) using rough set theory. The stimulation demonstrated that the accuracy of the noun phrase improved by the rough set was higher than another translation method; therefore, it is an effective machine identification method for English noun phrases, providing a basis for practical design. |
| Keywords: Noun Phrase, Machine Translation, Rough Set, WSJ Improvement of English-Chinese Translation Method by Feature Reduction and Rule Optimization Based on Rough Set Theory |
| DOI:https://doi.org/10.6025/jdim/2023/21/3/83-87 |
| Full_Text PDF 1.32 MB Download: 58 times |
| References: [1] Li, Yingjun. (2014). Status and Prospect of Machine Translation and Translation Technology Research. Chinese Science & Technology Translators Journal, 24-27. [2] Chen, Yun., Zhang, Penghua., Ren, Lihua. (2013). A Review on Machine Translation. Value Engineering, 174-176. [3] Feng, Ke. (2014). Computer Aided Translation. Brightness, 27, 289. [4] Li, Tianrui., Chen, Hongmei., Yang, Y. (2013). Rough Set Theory and Application. International Academic Development, 13-15. [5] Li, Xiang., Cheng, Yusheng., Ding, Meiwen. (2014). The Method of Bayesian Network Based on the Rough Set. Theory Journal of Anqing Teachers College (Natural Science Edition), 36-40. [6] Wang, Shuai. (2014). Review of Corpus Translation Development in China. Chinese Editorials, 29-32. [7] Yin, Le . (2014). Acquisition of Translation Knowledge in Machine Translation based on Corpus. Beijing Jiaotong University. [8] Xue, Huiwen. (2014). An Analysis on the Significance of Corpus to Translating Research. Reading Digest, 45. [9] Lu, Zhenghai. (2014). Webpage Machine Translation Based on Corpus. Anhui Literature (Second Half Month), 12, 42-43. |