Abstract
Language takes varied forms as used on different platforms. The instinctive nature of humans to use shorter message length, facilitating faster typing while maintaining semantic clarity, shapes the structure of a non-standard form of written text known as the texting language. The present work focuses on developing a Trie-based technique to model words in texting language for normalizing SMS text to Standard English text. The model is conceived and developed through systematic analysis of training data on user behaviour of texting language. Trie as a data structure is not only compact, but also is easy to manipulate for performing operations related to text normalization. Although Trie is a well-known data structure, its application to model SMS text has so far been unexplored. The results obtained using this model, despite the scheme being computationally cheap, are comparable with existing HMM based schemes that are available in the literature.
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