A Transliteration based Word Segmentation System for Shahmukhi Script

Word Segmentation is an important prerequisite for almost all Natural Language Processing (NLP) applications. Since word is a fundamental unit of any language, almost every NLP system first needs to segment input text into a sequence of words before further processing. In this paper, Shahmukhi word segmentation has been discussed in detail. The presented word segmentation module is part of Shahmukhi-Gurmukhi transliteration system. Shahmukhi script is usually written without short vowels leading to ambiguity. Therefore, we have designed a novel approach for Shahmukhi word segmentation in which we used target Gurmukhi script lexical resources instead of Shahmukhi resources. We employ a combination of techniques to investigate an effective algorithm by applying syntactical analysis process using Shahmukhi Gurmukhi dictionary, writing system rules and statistical methods based on n-grams models.