%D 2020
%A F. Baseer
%A J. Jaafar
%A I.B.A. Aziz
%A A. Habib
%X Urdu is among the most widely used languages in the world for verbal and written communication. Due to lack of optimized and user friendly native Urdu-script support on various platforms, it is mostly written in Romanized script in soft form. In our research, we have developed a refined Urdu lexicon using tokens with the highest frequency of occurrence in the data set. This data set is basically a raw corpus of colloquial Urdu written in Romanized script. The corpus was collected from volunteer participants who used this language as a mode of communication on the Internet and text massaging. The raw corpus is passed through a series of steps such as Prepossessing, Tokenization and Annotation before passing it to computationally extensive subsequent steps. Edit Distance and K-means Clustering techniques are used for identification of candidate tokens and their potential selection/ inclusion in the refined lexicon. We have also identified most commonly used tokens, candidate tokens and other lingual attributes from the data collected. Based on analysis, we have proposed a computational model for refined colloquial Romanized Urdu lexicon development. © 2020 IEEE.
%O cited By 0; Conference of 2020 International Conference on Computational Intelligence, ICCI 2020 ; Conference Date: 8 October 2020 Through 9 October 2020; Conference Code:164916
%L scholars12637
%K Computation theory; Computational methods; Intelligent computing, Computational model; Edit distance; K-means clustering techniques; Potential selection; Tokenization; Urdu lexicon; User friendly; Written communications, K-means clustering
%R 10.1109/ICCI51257.2020.9247814
%J 2020 International Conference on Computational Intelligence, ICCI 2020
%T Refined Urdu Lexicon Development K-Means Clustering Based Computational Model Using Colloquial Romanized Urdu Dataset
%P 57-62
%I Institute of Electrical and Electronics Engineers Inc.