Is the consonant primacy script-universal or script-specific? Evidence from non-Roman script Korean Hangul

2018 ◽  
Vol 32 (4) ◽  
pp. 1085-1106 ◽  
Author(s):  
Hye K. Pae ◽  
Sungbong Bae ◽  
Kwangoh Yi
Keyword(s):  
Roman Script ◽  
Korean Hangul

The Soviet Union Korean Hangul School Establishment and Operation

2016 ◽  
Vol 58 ◽  
pp. 3
Author(s):  
Choong-Nam Ji ◽  
Myeong-Ho Yang
Keyword(s):  
Soviet Union ◽  
The Soviet Union ◽  
Korean Hangul

scholarly journals Nominal Inflection of the Tutsa Language

2021 ◽  
Vol 10 (4) ◽  
pp. 138-140
Author(s):  
Hemanta Konch
Keyword(s):  
South East Asia ◽  
Language Family ◽  
Arunachal Pradesh ◽  
Folk Tales ◽  
North East ◽  
Nominal Inflection ◽  
Roman Script ◽  
A Minor ◽  
New Generation ◽  
Assamese Language

North-East is a hub of many ethnic languages. This region constitutes with eight major districts; like-Assam, Arunachal Pradesh, Nagaland, Manipur, Mizoram, Tripura, Meghalaya and Sikkim. Tutsa is a minor tribe of Arunachal Pradesh. The Tutsa was migrated from the place ‘RangkhanSanchik’ of the South-East Asia through ‘Hakmen-Haksan’ way to Arunachal Pradesh. The Tutsa community is mainly inhabited in Tirap district and southern part of Changlang district and a few people are co-exists in Tinsukia district of Assam. The Tutsa language belongs to the Naga group of Sino-Tibetan language family. According to the Report of UNESCO, the Tutsa language is in endangered level and it included in the EGIDS Level 6B. The language has no written literature; songs, folk tales, stories are found in a colloquial form. They use Roman Script. Due to the influence of other languages it causes lack of sincerity for the use of their languages in a united form. Now-a-days the new generation is attracted for using English, Hindi and Assamese language. No study is found till now in a scientific way about the language. So, in this prospect the topic Nominal Inflection of the Tutsa Language has been selected for study. It will help to preserve the language and also help in making of dictionary, Grammar and language guide book.

Correlates of Korean Hangul Acquisition Among Kindergartners and Second Graders

2005 ◽  
Vol 9 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Jeung-Ryeul Cho ◽  
Catherine McBride-Chang
Keyword(s):  
Second Graders ◽  
Korean Hangul

The systematization and typology of the instant messaging writing system used by young Israeli Arabs

2008 ◽  
Vol 11 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Sandy Habib ◽  
Dennis Kurzon
Keyword(s):  
Instant Messaging ◽  
Computer Mediated Communication ◽  
Writing System ◽  
Mediated Communication ◽  
Computer Mediated ◽  
Israeli Arabs ◽  
Roman Script

This study investigates a new writing system based on the Roman script that has been used by Israeli Arabs in Israel for about ten years. This system is associated with instant messaging (IM); people usually use it when sending SMSs or when utilizing any of the computer-mediated communication forms, such as Messenger. The paper focuses on the systematization and the typology of this writing system based on data collected from about 40 participants studying in the same school. The results show that most of the participants have used this system systematically, and that this system can be classified as a developing alphabet.

scholarly journals Development of Jawi Spelling Skills Mobile Applications, 'Oh Jawiku'

2019 ◽  
Vol 13 (07) ◽  
pp. 80
Author(s):  
Noor Asmina Binti Mohd Rashid ◽  
Shaharuddin Bin Md Salleh ◽  
Norah Binti Md Noor
Keyword(s):  
Mobile Learning ◽  
Mobile Applications ◽  
Mobile Application ◽  
Mean Value ◽  
The Past ◽  
Post Test ◽  
Main Medium ◽  
The Mean ◽  
Addie Model ◽  
Roman Script

<p class="0abstract"> In the past, Jawi script was the main medium of intermediation in Malaya. However, since the emergence of the Roman script, the use of Jawi script has been marginalized causing less awareness among some of the younger generation on the uniqueness of Jawi script. Therefore, a mobile application has been developed to attract students to explore Jawi script especially the spelling of Jawi words. This Jawi Spelling Skills mobile application used constructivism approach and ADDIE model during the development process. This study used quantitative one group pre-test and post-test research design that conducted among 20 students of a primary school. The result shows that the mean value of the post-test was higher than the pre-test which is 89.00 while the percentage of students who liked the Jawi Spelling Skills mobile learning application was high. In conclusion, the application of Jawi Spelling Skills Mobile Learning Application able to attract students to learn Jawi indirectly raised the dignity of Jawi script in the future besides supporting the 21st- century education.</p>

scholarly journals A clustering framework for lexical normalization of Roman Urdu

2020 ◽  
pp. 1-31
Author(s):  
Abdul Rafae Khan ◽  
Asim Karim ◽  
Hassan Sajjad ◽  
Faisal Kamiran ◽  
Jia Xu
Keyword(s):  
Language Processing ◽  
Clustering Algorithm ◽  
String Matching ◽  
Feature Learning ◽  
Similarity Function ◽  
Feature Based ◽  
Real World Datasets ◽  
Roman Script ◽  
Maximum Similarity ◽  
Textual Content

Abstract Roman Urdu is an informal form of the Urdu language written in Roman script, which is widely used in South Asia for online textual content. It lacks standard spelling and hence poses several normalization challenges during automatic language processing. In this article, we present a feature-based clustering framework for the lexical normalization of Roman Urdu corpora, which includes a phonetic algorithm UrduPhone, a string matching component, a feature-based similarity function, and a clustering algorithm Lex-Var. UrduPhone encodes Roman Urdu strings to their pronunciation-based representations. The string matching component handles character-level variations that occur when writing Urdu using Roman script. The similarity function incorporates various phonetic-based, string-based, and contextual features of words. The Lex-Var algorithm is a variant of the k-medoids clustering algorithm that groups lexical variations of words. It contains a similarity threshold to balance the number of clusters and their maximum similarity. The framework allows feature learning and optimization in addition to the use of predefined features and weights. We evaluate our framework extensively on four real-world datasets and show an F-measure gain of up to 15% from baseline methods. We also demonstrate the superiority of UrduPhone and Lex-Var in comparison to respective alternate algorithms in our clustering framework for the lexical normalization of Roman Urdu.

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