  Ranking functions in information retrieval are often used in search engines
to recommend the relevant answers to the query. This paper makes use of this
notion of information retrieval and applies onto the problem domain of cognate
detection. The main contributions of this paper are: (1) positional
segmentation, which incorporates the sequential notion; (2) graphical error
modelling, which deduces the transformations. The current research work focuses
on classification problem; which is distinguishing whether a pair of words are
cognates. This paper focuses on a harder problem, whether we could predict a
possible cognate from the given input. Our study shows that when language
modelling smoothing methods are applied as the retrieval functions and used in
conjunction with positional segmentation and error modelling gives better
results than competing baselines, in both classification and prediction of
cognates.
  Source code is at: https://github.com/pranav-ust/cognates
