【摘 要】
:
Term translation of Chinese historical classics is very difficult and time-consuming work,and using term alignment methods to extract term translation pairs is of great help for historical term transl
【机 构】
:
State Grid Info & Telecom Group Beijing China-Power Information Technology Co.,LTD,Beijing,China
【出 处】
:
第十八届中国计算语言学大会暨中国中文信息学会2019学术年会
论文部分内容阅读
Term translation of Chinese historical classics is very difficult and time-consuming work,and using term alignment methods to extract term translation pairs is of great help for historical term translation.However,the limited bilingual corpora resources of historical classics and special morphology of the ancient Chinese result in poor performance of term alignment.To this end,this paper proposes a historical term alignment method using modern Chinese as a pivot language.The method first identifies English terms by rules,then aligns them from English to modern Chinese and then from modern Chinese to ancient Chinese.The use of English-modern Chinese corpus and modern-ancient Chinese corpus instead of English-ancient Chinese corpus solves the shortage problem of the parallel corpus.Moreover,using modern Chinese as a pivot language effectively reduces the alignment errors caused by the abbreviations and the interchangeable characters of ancient Chinese.In the term alignment experiment on Shiji,our method outperformed the direct alignment method significantly,which proves the validity of our method.
其他文献
Sentence selection and summary generation are two main steps to generate informative and readable summaries.However,most previous works treat them as two separated subtasks.In this paper,we propose a
Learning multi-lingual sentence embeddings usually requires large scale of parallel sentences which are difficult to obtain.We propose a novel self-learning approach which is capable of learning multi
Online news platforms have attracted massive users to read digital news online.The demographic information of these users such as gender is critical for these platforms to provide personalized service
The Chinese Semantic Dependency Graph(CSDG)Parsing reveals the deep and fine-grained semantic relationship of Chinese sentences,and the parsing results have a great help to the downstream NLP tasks.Ho
Event detection(ED)task aims to automatically identify trigger words from unstructured text.In recent years,neural models with attention mechanism have achieved great success on this task.However,exis