  Suggesting similar questions for a user query has many applications ranging
from reducing search time of users on e-commerce websites, training of
employees in companies to holistic learning for students. The use of Natural
Language Processing techniques for suggesting similar questions is prevalent
over the existing architecture. Mainly two approaches are studied for finding
text similarity namely syntactic and semantic, however each has its draw-backs
and fail to provide the desired outcome. In this article, a self-learning
combined approach is proposed for determining textual similarity that
introduces a robust weighted syntactic and semantic similarity index for
determining similar questions from a predetermined database, this approach
learns the optimal combination of the mentioned approaches for a database under
consideration. Comprehensive analysis has been carried out to justify the
efficiency and efficacy of the proposed approach over the existing literature.
