$S^3$ - Statistical Sandhi Splitting

The problem of Sam. dhi-Splitting is central to computational processing of Sanskrit texts. Currently the best-known algorithm for this task, given a chunk, generates all possible splits and chooses the Maximum-a-Posteriori estimate as the final answer. Our contributions to the task of Sam. dhi-Splitting are two-fold. Firstly, we improve upon the current algorithm by proposing a principled modification of the posterior probability function to achieve better results. Secondly, we propose an algorithm based on Bayesian Word-Segmentation methods. We find that the unsupervised version of our algorithm achieves a better precision than the current algorithm with the original probabilistic model. We then present a supervised version of our algorithm that outperforms all previous methods/models.