论文标题
使用梵语语法改善具有数字词源的多语言国家的电子治理和移动治理
Improvement of electronic Governance and mobile Governance in Multilingual Countries with Digital Etymology using Sanskrit Grammar
论文作者
论文摘要
随着数字连接（WiFi，3G，4G）和数字设备访问互联网的巨大改善，现在已经到了最遥远的角落。农村人士可以轻松地从PDA，笔记本电脑，智能手机等轻松访问Web或应用程序。这是政府的机会，大量接触公民，获得反馈，将他们与E政策联系起来，而无需部署庞大的人，物质或资源。但是，随着农村人民倾向于并宁愿用母语互动，多语言国家的政府成功地实施政府（G2C）和政府公民（C2G）治理时面临着很多问题。向不同语言的说话者呈现对Web或应用程序的平等经验是一个真正的挑战。在这项研究中，我们已经解决了印度雅利安人说的网民所面临的问题，这通常也适用于任何语言家庭群体或亚组。然后，我们试图使用词源提供可能的解决方案。词源用于使用其根部形式将单词相关联。公元前5世纪，帕尼尼（Panini）写了阿斯塔迪亚（Astadhyayi）在其中描绘了经文或规则 - 根据人，时态，性别，数字等单词如何改变，后来在西方国家遵循这本书，也以相对较新的语言得出了语法。我们已经训练了系统从表面级别自动提取从表面级别提取或使用Panian晶格规则从表面级别提取的单词。我们已经测试了超过10000个孟加拉语动词的系统，并以98％的精度提取了根部。我们现在正在努力扩展程序，以成功地诱使任何语言的任何单词，并通过在人工神经网络中应用这些规则集将其关联。
With huge improvement of digital connectivity (Wifi,3G,4G) and digital devices access to internet has reached in the remotest corners now a days. Rural people can easily access web or apps from PDAs, laptops, smartphones etc. This is an opportunity of the Government to reach to the citizen in large number, get their feedback, associate them in policy decision with e governance without deploying huge man, material or resourses. But the Government of multilingual countries face a lot of problem in successful implementation of Government to Citizen (G2C) and Citizen to Government (C2G) governance as the rural people tend and prefer to interact in their native languages. Presenting equal experience over web or app to different language group of speakers is a real challenge. In this research we have sorted out the problems faced by Indo Aryan speaking netizens which is in general also applicable to any language family groups or subgroups. Then we have tried to give probable solutions using Etymology. Etymology is used to correlate the words using their ROOT forms. In 5th century BC Panini wrote Astadhyayi where he depicted sutras or rules -- how a word is changed according to person,tense,gender,number etc. Later this book was followed in Western countries also to derive their grammar of comparatively new languages. We have trained our system for automatic root extraction from the surface level or morphed form of words using Panian Gramatical rules. We have tested our system over 10000 bengali Verbs and extracted the root form with 98% accuracy. We are now working to extend the program to successfully lemmatize any words of any language and correlate them by applying those rule sets in Artificial Neural Network.
