Philosophy Inheritance: The Language Engineering Path of Translation Conception of Zhang Boran

doi:10.3969/j.issn.1674-8921.2023.02.006

Abstract

Abstract:

On the basis of the research of linguistic theory, it is the mission of language engineering to develop an information system taking natural language as medium, which is exactly the language engineering vision of translation put forward by Professor Zhang Boran. First, we deeply explore the speech recognition and discuss the semantic disambiguation by introducing phonetic chunking to improve the accuracy of speech recognition. Second, we propose a scheme to construct a terminology corpus and discuss the path of terminology corpus assisting translation. From the perspective of linguistics, AI machine translation is explored to provide enlightenment for the study of language engineering path and directions for the study of AI interpretation and translation.

Key words: Translation concept of Professor Zhang Boran, language engineering path, phonetic chunking, disambiguation, terminology corpus

CLC Number:

H059

ZHANG Di, CHENG Lulu. Philosophy Inheritance: The Language Engineering Path of Translation Conception of Zhang Boran[J]. Contemporary Foreign Languages Studies, 2023, 23(2): 74-81.

References 24

[1]	Bühler H. 1986. Linguistic (semantic) and extra-linguistic (pragmatic) criteria for the evaluation of conference interpretation and interpreters[J]. Multilingua (5): 231-236.
[2]	Correa E. A., A. A. Lopes, & R. A. Diego, 2018. Word sense disambiguation: A complex network approach[J]. Information Sciences 422-423: 103-113.
[3]	Jiang F., S, Xu & X. M. Chu, et al. 2018. MCDTB: A Macrolevel Chinese Discourse Tree Bank[C]. COLING:3493-3504.
[4]	Miller G. A. & J. A. Selfridge. 1950. Verbal context and the recall of meaningful material[J]. American Journal of Psychology 2(2):176-185.
[5]	Nattinger J. & J. De Carrico. 1992. Lexical Phrases and Language Teaching[M]. Oxford: Oxford University Press.
[6]	Olive J., C. Christianson & J. McCary. 2011. Handbook of Natural Language Processing and Machine Translation[M]. London: Springer.
[7]	Ye Y., Q. Wu & Y. Li, et al. 2013. Unknown Chinese word extraction based on variety of overlapping strings[J]. Information Processing & Management (2):497-512.
[8]	Zhang A. 2017. Simultaneous interpreting(SI): The holy grail of artificial intelligence:An SI practitioner’s perspective[J]. Lebende Sprachen 2(2): 253-268.
[9]	冯志伟. 2018. 机器翻译与人工智能的平行发展[J]. 外国语(上海外国语大学学报)(6): 35-48.
[10]	邓阳. 2014. 基于Andriod平台的语音控制系统的设计与实现[D]. 沈阳: 东北大学.
[11]	胡富茂、张克亮. 2018. 面向机器翻译的双语语块对应研究[J]. 外语电化教学(3): 83-87.
[12]	刘伟童、刘培玉、刘文锋, 等. 2019. 基于互信息和邻接熵的新词发现算法[J/OL]. [2022-11-01]. 计算机应用研究(1). http://www.arocmag.com/article/02-2019-05-017.html.
[13]	欧阳剑. 2016. 面向数字人文研究的大规模古籍文本可视化分析与挖掘[J]. 中国图书馆学报(2): 66-80.
[14]	王华树. 2016. 计算机辅助翻译实践[M]. 北京: 国防工业出版社.
[15]	王晓玉、李斌. 2017. 基于 CRFs 和词典信息的中古汉语自动分词[J]. 数据分析与知识发现(5): 62-70.
[16]	王远新. 2009. 语言学教程[M]. 北京: 中央民族大学出版社.
[17]	小河. 2001. 计算机技术与语言学的结合—细看机器翻译与语音识别[J]. 软件工程(2): 11-18.
[18]	杨锦锋、关毅、何彬, 等. 2016. 中文电子病历命名实体和实体关系标注体系及语料库构建[J]. 软件学报(11): 2725-2746.
[19]	杨寅冬、姚洁. 2019. 神经网络机器翻译之语境式学习[J]. 太原师范学院学报(自然科学版)(3): 26-29.
[20]	夭荣朋、许国艳、宋健. 2016. 基于改进互信息和邻接熵的微博新词发现方法[J]. 计算机应用(10): 2772-2776. doi: 10.11772/j.issn.1001-9081.2016.10.2772
[21]	张柏然. 1995. 语言资料库与双语词典编撰[J]. 辞书研究(1): 2-10.
[22]	张柏然. 2012. 译学卮言[M]. 南京: 南京大学出版社.
[23]	郑树泉. 2019. 工业智能技术与应用[M]. 上海: 上海科学技术出版社.
[24]	周梁、高鹏、丁鹏, 等. 2005. 语音识别准确率与检索性能的关联性研究[A].程学旗、王斌.第二届全国信息检索与内容安全学术会议论文集[C]. 北京: 清华大学出版社. 67-74.