Computational linguistic retrieval framework using negative bootstrapping for retrieving transliteration variants
M. M. Sufyan Beg, Shashi Shekhar, Dilip Kumar Sharma · International Journal of Computational Vision and Robotics · 2019
In NLP, one of the imperative and relatively less mature area is transliteration. During transliteration, issues like language identification, script specification arise in mixed script queries. To overwhelm these issues, we propose a new technique called negative bootstrapping with frequent matrix apriori for transliteration. Roman script is widely used in web search query for searching contents. The major challenge that the system face to process transliterated word is because of its existence in more than one form. The experimental evaluation has been done to check transliteration accuracy along with language identification against established methods. The paper offers a high-principled answer to handle multiple scripts used in a document leading to the problems of term matching and committing variations in spelling while searching the contents. The problem is modelled collectively with the deep-learning design and achieves significantly better results when applied to n-gram approach on the benchmark dataset.