Neural machine translation of chemical nomenclature between English and Chinese

Author:

Xu TingjunORCID,Chen Weiming,Zhou Junhong,Dai Jingfang,Li Yingyong,Zhao Yingli

Abstract

AbstractMachine translation of chemical nomenclature has considerable application prospect in chemical text data processing between languages. However, rule based machine translation tools have to face significant complication in rule sets building, especially in translation of chemical names between English and Chinese, which are the two most used languages of chemical nomenclature in the world. We applied two types of neural networks in the task of chemical nomenclature translation between English and Chinese, and made a comparison with an existing rule based machine translation tool. The result shows that deep learning based approaches have a great chance to precede rule based translation tools in machine translation of chemical nomenclature between English and Chinese.

Funder

Young Scientists Fund

CSDB

SGST

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Computer Graphics and Computer-Aided Design,Physical and Theoretical Chemistry,Computer Science Applications

Reference33 articles.

1. McNaught A (2002) Chemical nomenclature and structure representation. Chem Int 24:12–14. https://doi.org/10.1515/ci.2002.24.2.12b

2. Chemical Abstracts Service (2007) Naming and indexing of chemical substances for chemical abstracts. Appendix IV of CA Index Guide

3. Ikutoshi, Matsuura (2005) Development of a system for translation of chemical name into 2D-structure. 28th symposium on chemical information and computer science, 29–32

4. Lowe DM, Corbett PT, Murray-Rust P, Glen RC (2011) Chemical name to structure: OPSIN, an open source solution. J Chem Inf Model 51:739–753. https://doi.org/10.1021/ci100384d

5. Google Inc (2020) Google. https://www.google.com/

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3